复杂工业过程数据挖掘方法及其在铜锍吹炼中的应用研究
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摘要
以有色冶金过程为代表的复杂工业过程普遍具有多变量、非线性、大滞后、强耦合等特点,难以利用机理分析建立系统数学模型并实现优化操作与控制。我国的复杂工业过程的操作决策在较大程度上依靠人的经验,因此,相关生产过程能耗高、运行不稳定、原材料消耗大,在节能降耗、提高产品产量和质量等方面存在巨大的潜力。另一方面,随着工业基础自动化程度的逐步提高,多数生产单位积累了大量的生产过程历史数据,这些数据中可能蕴含有生产过程的运行规律、人工操作经验、优化操作模式等对操作决策和优化控制有用的信息,但因受数据分析技术水平和能力的限制,这些数据大多数未充分发挥作用。因此,研究从复杂工业过程数据中提取信息的数据挖掘方法具有重要的理论意义和巨大的应用价值。论文针对复杂工业过程数据挖掘方法及其应用的若干问题展开研究,主要研究内容及相关成果如下：
1.基于对复杂工业过程主要特点和优化决策问题的基本分析,提出了复杂工业过程数据挖掘的基本框架。该框架对复杂工业过程数据挖掘的定义、基本任务、一般实现过程和算法构成进行了规范,并强调了复杂工业过程机理分析在提高数据挖掘效率、确保数据挖掘结果的正确性方面的重要地位和作用,对该领域数据挖掘的实施和应用具有较大的指导意义。
2.鉴于数据挖掘用样本的质量直接影响挖掘结果的正确性,为提高挖掘用数据样本的质量,有效地处理样本集中的违规样本(即属性间匹配关系异常的样本),提出了一种基于小波分析的异常样本检测与修复方法。该方法利用小波变换的“高通滤波”和“时-频分析”特性,利用与样本对应的小波变换系数值检测和修复异常样本；构造了可直接用于低维异常样本检测与修复的低维小波变换系数的快速算法；应用“属性简约”思想,提出了基于非线性映射和小波分析的多维异常样本检测方法。理论分析和仿真效果表明,这种方法能有效地检测和修复数据挖掘对象中的异常样本,显著提高数据质量。
3.基于对模型效果和建模用数据质量间关系的深入分析,提出了基于数据质量约束的受限最优建模思想。通过定性、定量分析建模用数据的噪声强度、样本规模等数据质量因素对模型准确性的影响,指出：模型的训练误差存在最优值(称其为期望训练误差),该值可以根据数据质量信息近似估算,并可作为模型优化的判据。据此提出了“估算期望误差—比较实际误差与期望误差—调整模型结构”的优化建模思想,并分析了实现这一建模思想的技术重点和难点。受限最优建模思想为优化建模提供了一种不依赖检验数据的判据,可以显著提高优化建模的时间效率。论文将这一思想应用于神经网络、支持向量机两种模型的优化,取得了很好的效果。
4.针对神经网络建模效果对模型结构和训练方法敏感这一缺陷,提出了一种新的基于双网结构的神经网络优化建模方法。该方法兼具“结构修剪／增长法”和“提前终止法”两种传统的神经网络优化方法的优点,并克服了其不足：采用并行的双网结构,对两个子网络均采用“提前终止法”训练,既避免了网络“过拟合”,又较好地解决了“提前终止法”的“倾斜效应”;利用受限最优建模思想对两个子网络结构(即隐层节点)进行优化调整,既优化了网络结构,又在时间复杂度上远小于传统的基于交叉检验法的“结构修剪／增长法”。仿真结果表明,该方法的建模效果优于相关的传统方法。
5.针对支持向量机元参数较多、且缺乏优化依据的问题,提出了一种新的高效准确的支持向量机优化建模方法。基于对支持向量机三类参数之间耦合程度的分析,将其优化问题分解为超结构参数(核参数)优化和结构参数(不敏感参数和正则化参数)优化两个相对独立的子问题；提出了一种新的基于距离关系的核校准系数以优化核参数；运用受限最优建模思想优化支持向量机不敏感参数和正则化参数。同时,还提出了利用支持向量回归误差序列和应变量序列的相关性判断模型是否“过拟合”的方法。理论分析与仿真效果表明,该方法的优化效果与交叉检验法近似,但时间效率远高于交叉检验法。
6.在综合分析铜锍转炉吹炼过程机理、工艺操作制度和数据挖掘的应用条件等基础上,提出了基于数据挖掘的铜锍吹炼过程优化决策方法,并引入“支持度”和“置信度”等指标评价优化决策模型。基于本文在数据挖掘基础理论、基本方法方面的研究成果,利用某厂铜锍转炉吹炼过程积累的实际生产历史数据,建立了吹炼熔剂量和鼓风时间的优化决策模型。仿真实验表明,熔剂量优化决策模型和鼓风时间优化决策模型能够显著改善S1期的吹炼效果,具有明显的推广应用价值。建模过程体现了应用数据挖掘方法分析问题、解决问题、实现优化决策的思想,对实现其它复杂工业过程的优化决策具有重要的指导意义和显著的推广应用价值。
论文研究工作表明,数据挖掘方法在实现积累有大量历史生产数据的复杂工业过程的优化决策和优化控制方面具有显著的应用价值和广阔的应用前景。
The complex industrial processes represented by non-ferrous metallurgy generally have such characteristics as multi-variable, non-linear, long time-delay, strong coupling and so on. Therefore, they are difficult to be controlled optimally using system principle model. Decision and operation of domestic complex industrial processes rely on human's experience to a great extent, so some relevant production process consume too much energy and raw material, run unstably and have great potential in saving energy, improving product yield and quality and so on. On the other hand, with the development of industrial automation, lots of production process data are accumulated, these data maybe contain some useful information such as system's operation law, operator's experience, optimal operation pattern and so on, but they have not been made the best used because of the restriction of data analysis technology level. Therefore, studying the Complex Industrial Process Data Mining (CIPDM for short) is of great theoretical significance as well as application value. Some problems of CIPDM methods and their applications are studied in this dissertation, the main research contents and results are as follows:
1. Based on analysis on structure and basic optimization problems of complex industrial process, a basic framework of CIPDM is proposed. This framework normalizes the definition, basic tasks, general realization process and algorithm structure of CIPDM, stresses the roles of industrial process principle analysis in improving the efficiency and validity of CIPDM, and is of great guidance value for the implementation and application of CIPDM.
2. In view of the validity of results of data mining is directly impacted by the data quality, an approach based on wavelet analysis for detecting and amending anomalies in data set is proposed in order to improving the data quality. Taking full advantage of wavelet analysis' abilities of "high-pass filtering" and "time-frequency analysis", this approach detects and amends the anomalies according to the values of their wavelet transformation coefficients; fast computation algorithms for low dimensional wavelet transformation coefficient are proposed, which can be directly applied to process anomalies in low dimensional data set; integrating the means of attribute reduction, a method based on wavelet analysis and non-linear mapping is proposed to detect the anomalies in multi-dimension data set. Simulation experiments show that the approach is accurate and practical.
3. Based on the depth analysis on the relation between model performance and data quality, an idea of "optimal modeling restricted by data quality" is proposed. Through qualitatively and quantitatively analyzing the influence on the accuracy of the model due to data quality factors such as noise strength, sample size and so on, it is pointed out that training error of model has optimal value (be called as expectation training error), which can be estimated according to data quality information and be used as the criterion of optimal modeling. Therefore, a new optimal modeling idea, "estimate expectation training error firstly, then adjust model structure according to the difference between the expectation error and real error", is proposed, and the keystones and difficulties to realize this modeling idea are analyzed. This idea provides a new criterion not relying on the test data, so it can significantly improve the time efficiency of optimal modeling. In this dissertation, this idea is used to optimize artificial neural network (ANN) model and support vector machine (SVM) model, and achieves good performance.
4. In view of the performance of ANN is very sensitive to its structure and training method, a new optimal modeling method based on ANN with double-net structure is proposed. This method inherits the advantages of two kinds of traditional methods, those are "structure pruning/growing" and "early stopping", and overcomes their defects:the ANN is of parallel two sub-nets with same structure, which are both trained by "early stopping", therefore, "over-fitting" can be avoided as well as the "incline problem" of "early stopping" be solved; the structure of two sub-nets are adjusted according to the idea of restricted optimal modeling, therefore, the structure of ANN can be optimized with high time efficiency. Simulation experiments verify that the method has better performance than relational traditional methods.
5. In view of the problem that the parameters of SVM are relatively many and the optimization of which lacks theoretic basis, a new efficient and accurate optimal modeling method based on SVM is proposed. Based on the analysis on the coupling degrees among the three parameters of SVM, the parameters optimization problem is divided into two relatively independent optimization problems:kernel parameter optimization and structure parameters (that is intensive parameter and regularization parameter) optimization. A new Kernel Alignment Coefficient based on distance relation is introduced into kernel parameter optimization in this paper. Two structure parameters are proposed to be optimized synchronously according to the idea of "restricted optimal modeling". In addition, a method to evaluate the reasonability of the SVM model by distribution characteristics of the model errors in training set is proposed. Simulation results show that the method proposed in this paper is nearly as accurate as "cross validation" method, while much more rapid than "cross validation".
6. Process mechanism, operation technology regulation of copper-matte converting and application conditions of data mining are comprehensively analyzed, based on these, optimal decision methods based on data mining are proposed, two new evaluation indexes, named as support degree and confidence degree, are introduced into model evaluation. Using some research results of this dissertation in basic theory and methods of data mining, and according to historical data accumulated in production process of matte converting of a factory, optimal decision making models for flux adding amount and blasting time are built. Simulation results show that these two decision-making models can significantly improve the converting quality of S1 period, and have great popularization value. The modeling process reflects the means of analyzing and solving problems of optimal decision making by data mining method, which is of guiding significance for optimal decision making of other complex industrial processes.
This dissertation's research shows that data mining method is of great application value and broad application prospects in optimal decision and control of complex industrial processes which have accumulated a large number of historical production data.
1.基于对复杂工业过程主要特点和优化决策问题的基本分析,提出了复杂工业过程数据挖掘的基本框架。该框架对复杂工业过程数据挖掘的定义、基本任务、一般实现过程和算法构成进行了规范,并强调了复杂工业过程机理分析在提高数据挖掘效率、确保数据挖掘结果的正确性方面的重要地位和作用,对该领域数据挖掘的实施和应用具有较大的指导意义。
2.鉴于数据挖掘用样本的质量直接影响挖掘结果的正确性,为提高挖掘用数据样本的质量,有效地处理样本集中的违规样本(即属性间匹配关系异常的样本),提出了一种基于小波分析的异常样本检测与修复方法。该方法利用小波变换的“高通滤波”和“时-频分析”特性,利用与样本对应的小波变换系数值检测和修复异常样本；构造了可直接用于低维异常样本检测与修复的低维小波变换系数的快速算法；应用“属性简约”思想,提出了基于非线性映射和小波分析的多维异常样本检测方法。理论分析和仿真效果表明,这种方法能有效地检测和修复数据挖掘对象中的异常样本,显著提高数据质量。
3.基于对模型效果和建模用数据质量间关系的深入分析,提出了基于数据质量约束的受限最优建模思想。通过定性、定量分析建模用数据的噪声强度、样本规模等数据质量因素对模型准确性的影响,指出：模型的训练误差存在最优值(称其为期望训练误差),该值可以根据数据质量信息近似估算,并可作为模型优化的判据。据此提出了“估算期望误差—比较实际误差与期望误差—调整模型结构”的优化建模思想,并分析了实现这一建模思想的技术重点和难点。受限最优建模思想为优化建模提供了一种不依赖检验数据的判据,可以显著提高优化建模的时间效率。论文将这一思想应用于神经网络、支持向量机两种模型的优化,取得了很好的效果。
4.针对神经网络建模效果对模型结构和训练方法敏感这一缺陷,提出了一种新的基于双网结构的神经网络优化建模方法。该方法兼具“结构修剪／增长法”和“提前终止法”两种传统的神经网络优化方法的优点,并克服了其不足：采用并行的双网结构,对两个子网络均采用“提前终止法”训练,既避免了网络“过拟合”,又较好地解决了“提前终止法”的“倾斜效应”;利用受限最优建模思想对两个子网络结构(即隐层节点)进行优化调整,既优化了网络结构,又在时间复杂度上远小于传统的基于交叉检验法的“结构修剪／增长法”。仿真结果表明,该方法的建模效果优于相关的传统方法。
5.针对支持向量机元参数较多、且缺乏优化依据的问题,提出了一种新的高效准确的支持向量机优化建模方法。基于对支持向量机三类参数之间耦合程度的分析,将其优化问题分解为超结构参数(核参数)优化和结构参数(不敏感参数和正则化参数)优化两个相对独立的子问题；提出了一种新的基于距离关系的核校准系数以优化核参数；运用受限最优建模思想优化支持向量机不敏感参数和正则化参数。同时,还提出了利用支持向量回归误差序列和应变量序列的相关性判断模型是否“过拟合”的方法。理论分析与仿真效果表明,该方法的优化效果与交叉检验法近似,但时间效率远高于交叉检验法。
6.在综合分析铜锍转炉吹炼过程机理、工艺操作制度和数据挖掘的应用条件等基础上,提出了基于数据挖掘的铜锍吹炼过程优化决策方法,并引入“支持度”和“置信度”等指标评价优化决策模型。基于本文在数据挖掘基础理论、基本方法方面的研究成果,利用某厂铜锍转炉吹炼过程积累的实际生产历史数据,建立了吹炼熔剂量和鼓风时间的优化决策模型。仿真实验表明,熔剂量优化决策模型和鼓风时间优化决策模型能够显著改善S1期的吹炼效果,具有明显的推广应用价值。建模过程体现了应用数据挖掘方法分析问题、解决问题、实现优化决策的思想,对实现其它复杂工业过程的优化决策具有重要的指导意义和显著的推广应用价值。
论文研究工作表明,数据挖掘方法在实现积累有大量历史生产数据的复杂工业过程的优化决策和优化控制方面具有显著的应用价值和广阔的应用前景。
The complex industrial processes represented by non-ferrous metallurgy generally have such characteristics as multi-variable, non-linear, long time-delay, strong coupling and so on. Therefore, they are difficult to be controlled optimally using system principle model. Decision and operation of domestic complex industrial processes rely on human's experience to a great extent, so some relevant production process consume too much energy and raw material, run unstably and have great potential in saving energy, improving product yield and quality and so on. On the other hand, with the development of industrial automation, lots of production process data are accumulated, these data maybe contain some useful information such as system's operation law, operator's experience, optimal operation pattern and so on, but they have not been made the best used because of the restriction of data analysis technology level. Therefore, studying the Complex Industrial Process Data Mining (CIPDM for short) is of great theoretical significance as well as application value. Some problems of CIPDM methods and their applications are studied in this dissertation, the main research contents and results are as follows:
1. Based on analysis on structure and basic optimization problems of complex industrial process, a basic framework of CIPDM is proposed. This framework normalizes the definition, basic tasks, general realization process and algorithm structure of CIPDM, stresses the roles of industrial process principle analysis in improving the efficiency and validity of CIPDM, and is of great guidance value for the implementation and application of CIPDM.
2. In view of the validity of results of data mining is directly impacted by the data quality, an approach based on wavelet analysis for detecting and amending anomalies in data set is proposed in order to improving the data quality. Taking full advantage of wavelet analysis' abilities of "high-pass filtering" and "time-frequency analysis", this approach detects and amends the anomalies according to the values of their wavelet transformation coefficients; fast computation algorithms for low dimensional wavelet transformation coefficient are proposed, which can be directly applied to process anomalies in low dimensional data set; integrating the means of attribute reduction, a method based on wavelet analysis and non-linear mapping is proposed to detect the anomalies in multi-dimension data set. Simulation experiments show that the approach is accurate and practical.
3. Based on the depth analysis on the relation between model performance and data quality, an idea of "optimal modeling restricted by data quality" is proposed. Through qualitatively and quantitatively analyzing the influence on the accuracy of the model due to data quality factors such as noise strength, sample size and so on, it is pointed out that training error of model has optimal value (be called as expectation training error), which can be estimated according to data quality information and be used as the criterion of optimal modeling. Therefore, a new optimal modeling idea, "estimate expectation training error firstly, then adjust model structure according to the difference between the expectation error and real error", is proposed, and the keystones and difficulties to realize this modeling idea are analyzed. This idea provides a new criterion not relying on the test data, so it can significantly improve the time efficiency of optimal modeling. In this dissertation, this idea is used to optimize artificial neural network (ANN) model and support vector machine (SVM) model, and achieves good performance.
4. In view of the performance of ANN is very sensitive to its structure and training method, a new optimal modeling method based on ANN with double-net structure is proposed. This method inherits the advantages of two kinds of traditional methods, those are "structure pruning/growing" and "early stopping", and overcomes their defects:the ANN is of parallel two sub-nets with same structure, which are both trained by "early stopping", therefore, "over-fitting" can be avoided as well as the "incline problem" of "early stopping" be solved; the structure of two sub-nets are adjusted according to the idea of restricted optimal modeling, therefore, the structure of ANN can be optimized with high time efficiency. Simulation experiments verify that the method has better performance than relational traditional methods.
5. In view of the problem that the parameters of SVM are relatively many and the optimization of which lacks theoretic basis, a new efficient and accurate optimal modeling method based on SVM is proposed. Based on the analysis on the coupling degrees among the three parameters of SVM, the parameters optimization problem is divided into two relatively independent optimization problems:kernel parameter optimization and structure parameters (that is intensive parameter and regularization parameter) optimization. A new Kernel Alignment Coefficient based on distance relation is introduced into kernel parameter optimization in this paper. Two structure parameters are proposed to be optimized synchronously according to the idea of "restricted optimal modeling". In addition, a method to evaluate the reasonability of the SVM model by distribution characteristics of the model errors in training set is proposed. Simulation results show that the method proposed in this paper is nearly as accurate as "cross validation" method, while much more rapid than "cross validation".
6. Process mechanism, operation technology regulation of copper-matte converting and application conditions of data mining are comprehensively analyzed, based on these, optimal decision methods based on data mining are proposed, two new evaluation indexes, named as support degree and confidence degree, are introduced into model evaluation. Using some research results of this dissertation in basic theory and methods of data mining, and according to historical data accumulated in production process of matte converting of a factory, optimal decision making models for flux adding amount and blasting time are built. Simulation results show that these two decision-making models can significantly improve the converting quality of S1 period, and have great popularization value. The modeling process reflects the means of analyzing and solving problems of optimal decision making by data mining method, which is of guiding significance for optimal decision making of other complex industrial processes.
This dissertation's research shows that data mining method is of great application value and broad application prospects in optimal decision and control of complex industrial processes which have accumulated a large number of historical production data.
引文
[1]U Fayyad, R Uthursamy. Evolving Data Mining into Solutions For Insights[J], Communications of The ACM,2002,45(8):28-31
[2]U Fayyad, D Haussler, P Stolorz. KDD for science data analysis:Issues and examples[J], Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD96), USA:AAAI Press,1996:50-56
[3]J Han, M Kamber.数据挖掘概念与技术[M],范明,孟小峰译.北京：机械工业出版社,2001
[4]邵峰晶,于忠清.数据挖掘原理与算法[M],北京：中国水利水电出版社,2003
[5]U Fayyad, G Piatetsky-Shapiro, P Smyth. Knowledge Discovery and Data Mining. Towards a Unifying Framework[A]. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD'96)[C], USA:AAAI press,1996:82-88
[6]朱廷劭.数据挖掘及其在汉语文语转换中应用的研究[D],北京：中国科学院计算技术研究所,1999
[7]P Chapman, R Kerber, J. Clinton, etc. The CRISP-DM Process Model[R], http://www.spss.it/download/pub-paper.pdf
[8]G H John. Enhancements to the Data Mining Process[D], California:Standford University,1997
[9]R J Brachman, T Anand. the process of Knowledge Discovery in Databases:A Human-Centered Approach[A]. Advance In Knowledge Dicovery And Data Mining[C], USA:AAAI/MIT Press,1996:37-58
[10]Data Mining Group. PMML 2.0:Predictive Model Makeup Language [EB/OL]. http://www. dmg. org/v220/GeneralStructure. html
[11]Microsoft Corporation. Introduction to OLE DB for Data Mining[EB/OL]. http://www. microsoft. com/data/oledb/dm. html
[12]R Agrawal, T Imielinski, A Swami. Mining association rules between sets of items in large databases[A]. Proceedings of the ACM SIGMOD Conference on Management of data[C],1993.05:207-216
[13]I Rish. An empirical study of the naive Bayes classifier[A], Proceedings of IJCAI Workshop on Empirical Methods in Artificial Intelligence[C],2001:41-46
[14]周俊.铜闪速熔炼贫化电炉渣含铜的线性回归分析[J],矿冶,2003,12(2)： 58-62
[15]A K Jain, M N Murty, P J Flynn. Data clustering:A review[J], ACM Computing Surveys,1999,31(3):264-232
[16]P Giordani, H A L Kiers. A comparison of three methods for principal component analysis of fuzzy interval data[J], Computational Statistics& Data Analysis,2006, 51(1):379-397
[17]E Eskin. Anomaly Detection over Noisy Data using Learned Probability Distributions[A], Proceedings of the Seventeenth International Conference on Machine Learning[C],2000:255-262
[18]Judea Pearl. Fusion, propagation, and structuring in belief networks[J], Artificial Intelligence,1986,29(3):241-288.
[19]G F Cooper, C Yoo. Causal discovery from a mixture of experimental and observational data[A], Fifth Annual Conference on Uncertainty in Artificial Intelligence[C], Morgan Kaufmann,1999:116-125
[20]S Tong, D Koller. Active learning for structure in Bayesian networks[A]. Procceeding of the International Joint Conference on Artificial Intelligence[C]. Scattle, Washington:Morgan Kaufinann,2001:863-869
[21]K Murphy. Active learning of causal Bayes net structure[R]. Berkeley: University of California,2001
[22]J Tian, J Pearl. Causal discovery from changes[A], Seventeenth Annual Conference on Uncertainty in Artificial Intelligence[C], Washington,2001:512-521
[23]邢杰,萧德云.基于PCA的概率神经网络结构优化[J],清华大学学报(自然科学版),2008,48(1)：141-144
[24]林亚平,杨小林.快速概率分析进化算法及其性能研究[J],电子学报,2001,29(2)：178-181
[25]焦李成.神经网络系统理论[M],西安：西安电子科技大学出版社,1996
[26]M W Craven, J W Shavlik. Using Neural Networks for Data Mining[J], Future Generation Computer Systems,1998:345-350
[27]S Hawkins, H X He, G Williams. Outlier Detection Using Replicator Neural Networks[A]. Proceedings of the Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK02) [C], London,2002:170-180
[28]王宪保,周德龙,王守觉.基于仿生模式识别的构造型神经网络分类方法[J],计算机学报,2007,30(12)：2109-2114
[29]雷景生,马军,靳婷.基于分级神经网络的Web文档模糊聚类技术[J],计 算机研究与发展,2006,43(10)：1695-1699
[30]马君,刘小冬,孟颖.基于神经网络的城市交通流预测研究[J],电子学报,2009,37(5)：1092-1094
[31]S Mitra, K P Sankar, P Mitra. Data Mining in Soft Computing Framework:A Survey[J], IEEE Transactions on neural networks,2002,13(1):3-14
[32]U Anders, O Korn. Model selection in neural networks[J], Neural Networks, 1999,12(2):309-323
[33]S E Fahlman, C Lebiere. The cascade correlation learning architecture[A], Advances in Neural Information Processing Systems[C]. San Mateo, USA,1990
[34]M C Mozer, P Smolensky. Skeletonization:A technique for trimming the fat from a network via relevance assessment [A]. Advances in neural information processing(1) [C], Denver,1989:107-115
[35]Z Cataltepe, Y S Abumostafa, M Magdon-Ismail. No free lunch for early stopping[J], Neural Computation,1999,11(4):995-1009
[36]F D Foresee, M T Hagan. Gauss-Newton approximation to Bayesian reguiarization[C], Proceedings of the 1997 international joint conference on neural network,1997:1930-1935
[37]S I Gallant. Connectionist expert systems[J], Communications of the ACM,1988, 31(2):152-69
[38]A B Tickle, R Andrews, M Golea, etc. The truth will come to light:Directions and challenges in extracting the knowledge embedded within trained artificial neural networks[J], IEEE Transactions on Neural Networks,1998,9(1):1057-1068
[39]R Setiono. Extracting M-of-N rules from trained neural networks[J], IEEE Transactions on Neural Networks,2000,11(2):512-519
[40]何新贵.模糊知识处理的理论与技术[M],北京：国防工业出版社,1994
[41]J S R Jang. ANFIS:adaptive-network-based fuzzy inference system[J], IEEE Transaction Systems, Man, and Cybernetics,1993,23 (3):665-685
[42]L Yu, J Xiao. Trade-off between accuracy and interpretability:Experience oriented fuzzy modeling via reduced-set vectors[J], Computers and Mathematics with Applications,2009,57(6):885-895
[43]T A Johansen, R Shorten, R Murray-Smith. On the Interpretation and Identification of Dynamic Takagi-Sugeno Fuzzy Models[J], IEEE Transactions on Fuzzy Systems,2000,8(3):297-213
[44]Y Li, J M Deng, M Y Wei, Meaning and precision of adaptive fuzzy systems with Gaussian-type membership functions[J], Fuzzy Sets and Systems,2002,127(1): 85-97
[45]S M Zhou, J Q Gan. Low-level interpretability and high-level interpretability:a unified view of data-driven interpretable fuzzy system modeling[J], Fuzzy Sets and Systems,2008,159(23):3091-3131
[46]E Hullermeier. Fuzzy methods in machine learning and data mining:Status and prospects[J], Fuzzy Sets and Systems,2005,156(3):387-406
[47]E Hullermeier. Fuzzy sets in machine learning and data mining[J], Applied Soft Computing. (2008), doi:10.1016/j.asoc.2008.01.004
[48]张学工.关于统计学习理论与支持向量机[J],自动化学报,2000,26(1)：32-42
[49]B E Boser, I M Guyon, V N Vapnik. A training algorithm for optimal margin classifiers[C], Proceedings of the fifth annual workshop on Computational learning theory, USA,1992:144-152
[50]S Amari, S Wu. Improving support vector machine classifiers by modifying kernel functions[J], Neural Networks,1999,12 (6):783-789
[51]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M],北京：科学出版社，2004
[52]Z Pawlak. Rough sets[J], International Journal of Information and Computer Science,1982,11(5):341-356
[53]张文修,吴伟志.粗糙集理论介绍和研究综述[J],模糊系统与数学,2000,14(4)：1-11
[54]J G Bazan, M S Szczuka, A Wojna, etc. On the Evolution of Rough Set Exploration System[A], Rough Sets and Current Trends in Computing[C],2004: 592-601
[55]J G Bazan, M S Szczuka, J Wroblewski. A New Version of Rough Set Exploration System[A], Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing[C],2002:397-404
[56]曾黄麟.智能计算——关于粗集理论、模糊逻辑、神经网络的理论及其应用[M],重庆：重庆大学出版社,2004
[57]J W Grzymala-Busse. LERS:a system of learning for examples based on rough sets[Ch], Intelligent Decisions Support:Handbook of applications and advances of rough sets theory,1992, Kluwer Academic Publishers, Boston:3-18
[58]J W Grzymala-Busse. A New Version of the Rule Induction System LERS[J], Fundamenta Informaticae,1997,31(1):27-39
[59]张曼,吴涛,王容川,等.基于粗糙集理论和构造型神经网络的分类算法研究[J],模式识别与人工智能,2004,17(3)：362-367
[60]黎明,张化光.基于粗糙集的神经网络建模方法研究[J],自动化学报,2002,28(1)：27-33
[61]张雪峰,田晓东,张庆灵.基于粗糙集理论和层次分析的数据约简[J],东北大学学报(自然科学版),2008,29(01)：21-24
[62]S T Hsieh, T Y Sun, C C Liu. Potential offspring production strategies:An improved genetic algorithm for global numerical optimization[J], Expert Systems with Applications,2009,36(8):11088-11098
[63]P K Tripathi, S Bandyopadhyay, S K Pal. Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients[J], Information Sciences,2007,177(22):5033-5049
[64]B Naderi, M Khalili, R Tavakkoli-Moghaddam. A hybrid artificial immune algorithm for a realistic variant of job shops to minimize the total completion time[J], Computers& Industrial Engineering,2009,56(4):1494-1501
[65]D X Yang, G Li, G D Cheng. On the efficiency of chaos optimization algorithms for global optimization Chaos[J], Solitons& Fractals,2007,34(4):1366-1375
[66]N Jiang, Z Y Zhao, L Q Ren. Design of structural modular neural networks with genetic algorithm[J], Advances in Engineering Software,2003,34(1):17-24
[67]卢荣德,陈宗海,王雷.思维模拟在复杂工业过程建模中应用的综述[J],信息与控制.2002,3 1(4)：346-352
[68]王凌,王雄,金以慧.MES——流程工业CIMS发展的关键[J],化工自动化仪表,2001,28(4)：1-5
[69]胡志坤,桂卫华,彭小奇,等.过程数据挖掘及进展[J],控制工程,2004,11(S)：1-4
[70]胡志坤,桂卫华,彭小奇,等.有色金属冶金过程数据挖掘[J],有色金属,2003,33(2)：1-4
[7l]刘洪霖.化工冶金过程人工智能优化[M],北京：冶金工业出版社,1999
[72]F R A Jorgensen, B J Eliot. Flash furnace reaction shaft evaluation through simulation[C]. AUSIMM International conference on Extractive Metal of Gold and base Metals,1992:387-389
[73]梅炽,王前普,彭小奇,等.有色冶金炉窑的仿真与优化[J],中国有色金属学报,1996,6(4)：19-23.
[74]S Goto. Equilibrium calculations between matte, slag and gaseous phases in copper smelting[C], Copper Metallurgy Practice and Theory,1974:23-34
[75]S Goto. The Application of Thermodynamic Calculations to Converter Practice[C], Copper and Nickel Converters(New Orleans),1979:33-55
[76]P Tan, C Zhang. Computer model of copper smelting process and distribution behaviors of accessory elements[J], Journal of Central South University of Technology,1997.4(1):36-41.
[77]M Nagamori, P J Mackey. Thermodynamics of copper matte converting-part I: Fundamentals of the Noranda process[J], Metallurgy Transactions,1978,9(2): 255-271
[78]M Nagamori, W J Errington, P J Mackey, etc. Thermodynamic simulation model of the isasmelt process for copper matte[J], Metallurgical and materials transactions, 1994,25(6):839-853
[79]G Eriksson. Thermodynamic studies of high temperature equilibria. III. SOLGAS, a computer program for calculating the composition and heat condition of an equilibrium mixture[J], Acta Chem Scand,1971,25(7):2651-2657
[80]G Eriksson,. Thermodynamic studies of high temperature equilibria. XII. SOLGASMIX, a computer program for calculation of equilibrium compositions in multiphase systems[J], Chem Scr,1975,8(3):100-103
[81]B Bjorkman, G Eriksson. Quantitative Equilibrium Calculations on Conventional Copper Smelting and Converting[J], Canadian Metallurgical Quarterly,1982,21(4): 329-337
[82]R P Cherian, L N Smith, P S Midha. A neural network approach for selection of powder metallurgy materials and process parameters[J], Artificial Intelligence in Engineering,2000.14(1):39-44
[83]R Cass, B Radl. Adaptive process optimization using functional-link networks and evolutionary optimization[J], Control Engineering Practice,1996,4(11): 1579-1584
[84]R Parra, F Kongoli, R Parada. New approach for the optimization of copper concentrates flash combustion through the control of the blends and slag composition[A], Paper presented at:Modeling, Control, and Optimization in Ferrous and Non-Ferrous Industry as held at the Materials Science& Technology 2003 Conference[C], Chicago, USA,2003:107-115
[85]王玉曾,张英林,李青茹.有色冶金窑炉中的神经网络过程控制[J],兰州 大学学报(自然科学版),1998.34(4)：69-72
[86]万维汉,杨金义.镍闪速熔炼过程的模糊动态质量模型与控制[J],西安交通大学学报,2000.34(3)：54-59
[87]万维汉,袁永发.镍闪速熔炼过程的模糊建模[J],冶金自动化,2000,24(2)：9-12
[88]曾青云,周立,汪金良.基于自适应模糊神经网络的铜闪速熔炼冰铜温度模型研究[J],有色金属(冶炼部分),2007(2)：2-4,9
[89]曾青云,汪金良.铜闪速熔炼神经网络模型的建立[J],南方冶金学院学报,2003,24(5)：15-18
[90]彭小奇.镍熔炼节能降耗、智能决策技术和熔炼车间计算机信息集成网络的开发与应用[D],中南大学,长沙,1998
[91]胡燕瑜,桂卫华,李勇刚,等.基于BP神经网络的熔融锌液流量检测[J],有色金属,2003,55(3)：143-146.
[92]C Yang, G Deconinck, W Gui, etc. An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity[J], IEEE Transactions on Neural Networks,2002,13(1):229-236.
[93]陈晓方,桂卫华,蔡自兴,等.过程控制中的智能集成建模方法[J],系统仿真学报,2001,13(s)：8-11
[94]王雅琳,桂卫华.自适应监督式分布神经网络及其工业应用[J],控制与决策,2001,16(5)：549-552
[95]杜玉晓,吴敏,岑丽辉,等.铅锌烧结过程的集成建模方法及智能优化算法[J],中国有色金属学报,2004,25(8)：1458-1463
[96]杜玉晓,吴敏,桂卫华.铅锌烧结过程的智能集成优化控制系统[J],中南工业大学学报(自然科学版),2003,34(4)：345-349
[97]杜玉晓,吴敏,桂卫华.铅锌烧结过程智能集成优化控制技术[J],控制与决策,2004,19(10)：1091-1096
[98]中国有色金属科技信息网.智能集成优化控制技术及其在锌电解和炼焦配煤过程中的应用[EB/OL],中国有色金属工业技术开发交流中心,2007.10
[99]李劫,王前普,肖劲,等.预焙铝电解槽智能模糊控制系统[J],中国有色金属学报,1998，8(3)：557-562
[100]边友康,刘钢,丁凤其,等.大型预焙铝电解槽现代工艺技术条件的选择与实现[J],轻金属,2000(11)：34-38
[101]肖劲,张泰山,刘业翔,等.铝电解槽点式下料的专家模糊控制方法[J],中南工业大学学报(自然科学版),1998,29(1)：32-35
[102]彭小奇,梅炽,周孑民,等.多变量模糊控制模型辨识方法及其在矿热电炉决策支持系统中的应用[J],控制理论与应用,1994,11(5)：582-587
[103]彭小奇,梅炽,周孑民,等.多变量系统的模糊神经网络控制模型及其应用[J],控制理论与应用,1995.12(3)：351-357
[104]R Sutinen, Causticizing plant and lime kiln computer control[J], Pulp and Paper, 1981,82(8):90-95
[105]高玉琦,李友善.水泥回转窑的计算机控制[J],自动化学报,1991,17(2)：166-173
[1061燕延,周彦萍.回转窑模糊控制系统[J],河北省科学院学报,1999,16(1)：32-34
[107]姚维,诸静.水泥回转窑生产过程的模糊控制[J],化工自动化及仪表,2000,27(2): 15-18
[108]章兢.回转窑集成智能控制系统[J],电工技术学报,2002,17(4)：62-66
[109]A Mrozek, Z Pawlak. Use of rough sets and decision tables for implementing rule-based control of industrial processes[J], Bulletin of the Polish Academy of Sciences,1986,34(5-6):357-371
[110]高赞,侯嫒彬.基于粗糙集理论的控制规则提取方法[J],工矿自动化,2003(4)：12-14
[111]张晖,吴斌,刘知贵.基于粗糙集理论的控制规则生成方法[J],计算机工程与应用,2003.39(13)：98-100
[112]常晓艳,刘振娟.基于粗糙集属性约简的过程控制规则提取[J],仪器仪表学报,2004，25(z1)：881-883
[113]崔庆安.基于粗糙集的焦炉加热工序控制规则提取[J],化工自动化及仪表,2005,32(6)：20-23
[114]黎明,张化光,何希勤.基于粗糙集的模糊模型辨识方法[J],东北大学学报(自然科学版),2000,21(5)：480-483
[115]刘璨,陈统坚,彭永红,等.基于粗集理论的模糊神经网络建模方法研究[J],中国机械工程,2001,12(11)：1256-1259
[116]陈双叶,易继锴.基于粗糊集理论的模糊模型及其在复杂控制系统中的应用[J],控制与决策,2002,17(B11)：747-751
[117]袁兵,江丽,朱宏辉.基于粗糙集理论的模糊控制规则的获取方法[J],武汉理工大学学报(交通科学与工程版),2005(3)：357-371
[118]陈泽华,谢克明.利用粗糙集获取模糊控制规则[J],太原理工大学学报,2003,34(3)：258-260
[119]黄金杰,李士勇,左兴权.一种T-S型粗糙模糊控制器的设计与仿真[J],系统仿真学报,2004,16(3)：480-484
[120]王介生,战红仁,王伟.基于粗糙集的TS模糊神经网络在回转窑烧结过程中的应用[J],华东理工大学学报(自然科学版),2006,32(007)：796-800
[121]C G Knickerbocker, R L Moore, L B Hawkinson, etc. The PICON expert system for process control[A], the 5th international workshop[C], Avignon, Agence de l'Informatique Paris La Defense, France,1986:59-66
[122]M Gallanti, L Tomada, R Tarli. Intelligent on-line system for cycle chemistry diagnostics in power plants[A], Proceedings of the American Power Conference[C], Chicago, IL, United States,1990:356-362
[123]B Porter, A H Jones, C B McKeown. Real-time expert tuners for PI controllers[A], IEE Proceedings D. Control Theory and Applications[C],1987: 260-263
[124]张学东,寇晓军.水泥回转窑实时专家控制系统的研制[J],中国矿业,2000,9(3)：82-84
[125]吴敏,唐朝晖.锌湿法冶炼电解过程的神经网络专家控制[J],自动化学报,2001,27(6)：867-869
[126]刘晓颖,桂卫华.铅锌冶炼过程的故障诊断神经网络专家系统[J],上海海运学院学报,2001,22(003)：89-91
[127]邓宏贵,罗安,丁家峰,等.递阶智能控制在电弧炉控制系统中的应用[J],电力系统及其自动化学报,2003,15(004)：51-54
[128]王春林,周昊,李国能,等.大型电厂锅炉NO_x排放特性的支持向量机模型[J], 浙江大学学报(工学版),2006,40(10)：1787-1791
[129]李丽娜,侯朝桢.基于支持向量机(SVM)的工业过程辨识[J].北京理工大学学报,2003,23(5)：569-571
[130]张立岩,岳恒,张君,等.基于最小二乘—支持向量机的制粉过程煤粉细度软测量模型[J].清华大学学报(自然科学版),2007,47(S2)：1932-1935
[131]张朴,万磊,孔力,等.基于支持向量机的激光焊接过程质量监测[J],华中科技大学学报(自然科学版),2007,35(7)：62-63
[132]F Torre, H P Wynn, P Corbett, etc. Data mining issues on modern multivariate online industrial process control[A], Paper presented at:7th Scandinavian Symposium on Chemometrics[C], Copenhagen, Denmark, John Wiley& Sons, Inc.,2001
[133]H Mori. State-of-the-Art Overview on Data Mining in Power Systems[A],2006 Power Systems Conference and Exposition, PSCE'06.2006 IEEE PES[C], Atlanta, GA,2006:33-34.
[134]F V D Berg. SSC7:7th Scandinavian Symposium on Chemometrics[EB/OL]. John Wiley& Sons, Inc.
[135]P Butler.过程控制的革命性剧变：知识系统提高用户的经营业绩[J],数字石油和化工,2006,1：71-72
[136]倪建军,马小平,王耀才.数据挖掘技术在工业控制系统中的应用研究[J],工业控制计算机,2004,17(3)：5-5,8
[137]朱群雄,麻德贤.过程工业新热点——数据挖掘[J],数字化工,2003(10)：38-39
[138]王耀南,宋明.复杂工业系统的广义知识模型与智能建模[J],中南工业大学学报,2003,34(4)：335-341
[139]刘业翔,陈湘涛,张更容,等.铝电解控制中灰关联规则挖掘算法的应用[J],中国有色金属学报,2004,14(3)：494-498
[140]铁军,朱旺喜.数据挖掘技术在铝电解生产中的应用[J],有色金属,2003,55(1): 56-59
[141]马元元,张南翔.增量关联规则在大型火力发电厂实时控制中的应用[J],工业控制计算机,2000,13(1)：14-15
[142]谭光兴,杜启亮,毛宗源.基于克隆选择的锌钡白煅烧过程数据挖掘[J],计算机工程与应用,2006,42(35)：211-213,218
[143]胡志坤.复杂有色金属熔炼过程操作模式智能优化方法[D],中南大学,长沙，2005
[144]胡志坤,桂卫华,彭小奇.有色冶金过程的数据挖掘[J],有色金属,2003,55(2)：47-50.
[145]宋彦坡.数据挖掘技术及其在铜转炉吹炼过程优化中的应用[D],中南大学,长沙,2005
[146]宋彦坡,彭小奇.数据挖掘技术及其在工业生产中的应用[J],计算机测量与控制,2004,12(10)：944-947
[147]卢勇,徐向东.数据挖掘与锅炉负荷多模型自适应控制研究[J],电站系统工程,2003,19(4)：49-51
[148]卢勇,徐向东,数据挖掘技术在热电厂过程控制与优化中的应用研究[J],电站系统工程,2003,19(2)：48-50
[149]李建强,牛成林,刘吉臻.数据挖掘技术在火电厂优化运行中的应用[J],动力工程,2006,26(6)：830-835
[150]吴少敏,陈贻龙.实用数据挖掘系统[J],冶金自动化,2002,26(1)：6- 10
[151]吴少敏,冯建生.宝钢数据挖掘系统[J],宝钢技术,2001(1)：43-47.
[152]吴少敏,冯建生.数据挖掘技术及其应用[J],冶金自动化,2001,25(6)：5-8
[153]高俊.模糊控制查询表的数据挖掘研究[J],计算机工程与应用,2004,40(33)：209-211
[154]赵妍,逢玉俊,文东丽.从样本数据中提取模糊规则的算法研究[J],石油化工高等学校学报,2004,17(3)：83-88
[155]丁彦明,庞维诚.数据库知识发现技术在连铸生产中的应用探索[J],铸造设备研究,2002(1)：13-15
[156]杨杰,叶晨洲.用于建模、优化、故障诊断的数据挖掘技术[J],计算机集成制造系统,2000,6(5)：72-76
[157]杨学瑜,宋晓娟,顾合英.数据挖掘在选煤厂过程控制中的应用[J],煤炭科学技术,2004,32(5)：21-24
[158]吴以凡,艾丽君,欧阳树生,等.面向钢铁生产过程质量控制的动态数据挖掘方法[J],冶金自动化,2006,30(4)：6-10
[159]邬成新.SAS数据挖掘在钢铁终轧温度控制中的应用[J],控制工程(沈阳),2005,12(6)：590-592
[160]张运陶,杨晓丽.轻烃回收装置数据挖掘及生产优化[J],计算机与应用化学,2005,22(7)：555-560
[161]刘敦楠,何光宇,范旻,等.数据挖掘与非正常日的负荷预测[J],电力系统自动化,2004,28(3)：53-57
[162]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J],电力系统自动化,2004,28(17)：l-11
[163]KDnuggets. Polls:Data preparation part in data mining projects,2003.10, http://www.kdnuggets.com/polls/2003/data_preparation.htm
[164]朱祖泽,马克毅.铜冶金学[M],昆明：云南科技出版社,1995
[165]任鸿久,王立川.有色金属提取冶金手册·铜镍卷[M],北京：冶金工业出版社,2000
[166]毕诗文,于海燕,杨毅宏,等.拜耳法生产氧化铝[M],北京：冶金工业出版社,2007
[167]毕诗文.氧化铝生产工艺[M],北京：化学工业出版社,2006
[168]梅炽.冶金传递过程原理[M],长沙：中南工业大学出版社,1987
[169]A Famili, W M Shen, R Weber, etc. Data Preprocessing and Intelligent Data Analysis[J], Intelligent Data Analysis,1997,1(1):3-23
[170]E Rahm, H D Hong. Data cleaning:problems and current approaches[J], IEEE Data Engineering Bulletin,2000,23(4):3-13
[171]颜钰芬,徐明钧.数理统计[M],上海：上海交通大学出版社,1992
[172]汪荣鑫.数理统计[M],西安：西安交通大学出版社,1986
[173]M R Chmielewski, J W Grzymala-Busse. Global Discretization of Continuous Attributes as Preprocessing for Machine Learning[J], International Journal of Approximate Reasoning,1996:320-331
[174]A Madkiewicz, W Ratajczak. Principal components analysis (PCA)[J], Computers& Geosciences,1993,19(3):303-342
[175]S Wold, J Trygg, A Berglund, etc. Some recent developments in PLS modeling[J], Chemometrics and Intelligent Laboratory Systems,2001,58(2): 131-150
[176]J W Sammon. A Nonlinear Mapping for Data Structure Analysis[J], IEEE Transactions on Computers,1969,18(5):401-409
[177]D Hawkins. Identification of Outliers[M], London:Chapman and Hall,1980
[178]E Eskin. Anomaly Detection over Noisy Data using Learned Probability Distributions[A], Proc.17th International Conf. on Machine Learning[C], San Francisco, CA, USA,2000:255-262
[179]E M Knorr, R T Ng. Algorithms for mining distance-based outliers in large datasets[A], Proceedings of the 24th International Conference on Very Large Data Bases[C], New York:Morgan Kaufmann,1998:392-403
[180]E M Knorr, R T Ng. Finding intensional knowledge of distance-based outliers[A], Proceedings of the 25th International Conference on Very Large Data Bases[C], Edinburgh, Scotland:Morgan Kaufmann,1999:211-222
[181]M M Breunig, H P Kriegel, R T Ng, etc. LOF:identifying density-based local outliers[A], Proceedings of the ACM SIGMOD International Conference on Management of Data[C], Dallas, Texas:ACM Press,2000:93-104
[182]M M Breunig, H P Kriegel, R T Ng, etc. OPTICS-OF:identifying local outliers[A], Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases[C], Lecture Notes in Computer Science (LNAI 1704), Prague:Springer,1999:262-270
[183]M F Jiang, S S Tseng, C M Su. Two-phase clustering process for outliers detection[J], Pattern Recognition Letters,2001,22(6-7):691-700
[184]S Hawkins, H X He, G Williams, etc. Outlier Detection Using Replicator Neural Networks[J], Proceedings of the Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK02), London, UK,2002:170-180
[185]A Arning, R Agrawal, P Raghavan. A linear method for deviation in large database[J], Conf. Data Mining and Knowledge Discovery(KDD96), Portland,1996: 164-169
[186]H G Stark. Continuous wavelet transform and continuous multiscale analysis[J], Journal of Mathematical Analysis and Applications,1992,169(1):179-196
[187]杨福生.小波变换的工程分析与应用[M],北京：科学出版社,2000
[188]B George, N R Draper. Empirical Model-Building and Response Surfaces[M], New York:Wiley,1987
[189]A Ashok. Analog and digital signal processing(Second Edition)[M], BROOKS/ COLE publishing company,1999
[190]L Devroye, L Gyorfi, D Schafer, etc. The estimation problem of minimum mean squared error[J], Statistics and Decisions,2003,21:15-28
[191]M J Buckley, G K Eagleson. The estimation of residual variance in nonparametric regression[J], Biometrika,1988,75(2):189-199
[192]M H Radfar, R M Dansereau, A Sayadiyan. Nonlinear minimum mean square error estimator for mixture-maximisation approximation[J], Electronics Letters,2006, 42(12):724-725
[193]J Immerkaer. Fast noise variance estimation[J], Computer Vision and Image Understanding,1996,64(2):300-302
[194]V Vapnik, A Chervonenkis. Necessary and sufficient conditions for the uniform convergence of means to their expectations[J], Theory of Probability and its Applications,1981,26(3):532-553
[195]K M Hornik, M Stinchcombe. Multilayer feed-forward networks are universal approximators[J]. Neural Networks,1989,2:359-366
[196]Y L Cun, J S Denker, S A Solla. Optimal brain damage[A]. Advances in neural information processing (2) [C], Denver,1990:598-605
[197]P F Konstantinos. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms[J]. Neural Networks, 2005,18(7):934-950
[198]S Haralambos, A Alex, M Stefanos, etc. A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms[J]. Computers& Chemical Engineering,2004,28(1-2):209-217
[199]J B Yu, S Jn Wang, L F Xi. Evolving artificial neural networks using an improved PSO and DPSO[J], Neurocomputing,2008,71(4-6):1054-1060
[200]Y Chauvin. A back-propagation algorithm with optimal use of hidden units[A]. Advances in neural information processing(2) [C], Denver,1990:642-649
[201]W L Buntine, A S Weigend. Bayesian back-propagation[J]. Complex Systems, 1991,5:603-643
[202]V Vapnik. The nature of statistical learning theory[M], Springer Verlag,1995
[203]N Cristianini, J Kandola, A Elisseeff, etc. On kernel target alignment[J], Advances in Neural Information Processing Systems,2002,14:367-373
[204]J Kandola, J Shawe-Taylor, N Cristianini. On the Extensions of Kernel Alignment[R],2002, http://www.neurocolt.org
[205]A J Smola, N Murata, B Scholkopf, etc. Asymptotically optimal choice of epsion-loss for support vector machines[A], Proceedings of the 8th International Conference on Artificial Neural Networks[C], Skovde, Sweden,1998,1:105-110
[206]J T Kwok, I W Tsang. Linear dependency between E and the input noise in E-support vector regression[J], IEEE Transactions on Neural Networks,2003,14(3): 544-553
[207]V Cherkassky, Y Q Ma. Practical selection of SVM parameters and noise estimation for SVM regression[J], Neural Networks,2004,17(1):113-126
[208]D Mattera, S Haykin. Support vector machines for dynamic reconstruction of a chaotic system[Ch], in:B. Scholkopf, J. Burges, A. Smola, ed., Advances in Kernel Methods:Support Vector Machine[M], MIT Press,1999
[209]江铜贵溪冶炼厂.内部培训教材·熔炼车间·转炉[M]，贵溪：贵溪冶炼厂,2000
[210]李文超.冶金与材料物理化学[M],北京：冶金工业出版社,2001
[211]周土平,石东坡,陈春林,等.贵冶铜转炉工业实验及物质平衡数据分析[J],有色金属(冶炼部分),1998,19(3)：7-9,18
[2]U Fayyad, D Haussler, P Stolorz. KDD for science data analysis:Issues and examples[J], Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD96), USA:AAAI Press,1996:50-56
[3]J Han, M Kamber.数据挖掘概念与技术[M],范明,孟小峰译.北京：机械工业出版社,2001
[4]邵峰晶,于忠清.数据挖掘原理与算法[M],北京：中国水利水电出版社,2003
[5]U Fayyad, G Piatetsky-Shapiro, P Smyth. Knowledge Discovery and Data Mining. Towards a Unifying Framework[A]. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD'96)[C], USA:AAAI press,1996:82-88
[6]朱廷劭.数据挖掘及其在汉语文语转换中应用的研究[D],北京：中国科学院计算技术研究所,1999
[7]P Chapman, R Kerber, J. Clinton, etc. The CRISP-DM Process Model[R], http://www.spss.it/download/pub-paper.pdf
[8]G H John. Enhancements to the Data Mining Process[D], California:Standford University,1997
[9]R J Brachman, T Anand. the process of Knowledge Discovery in Databases:A Human-Centered Approach[A]. Advance In Knowledge Dicovery And Data Mining[C], USA:AAAI/MIT Press,1996:37-58
[10]Data Mining Group. PMML 2.0:Predictive Model Makeup Language [EB/OL]. http://www. dmg. org/v220/GeneralStructure. html
[11]Microsoft Corporation. Introduction to OLE DB for Data Mining[EB/OL]. http://www. microsoft. com/data/oledb/dm. html
[12]R Agrawal, T Imielinski, A Swami. Mining association rules between sets of items in large databases[A]. Proceedings of the ACM SIGMOD Conference on Management of data[C],1993.05:207-216
[13]I Rish. An empirical study of the naive Bayes classifier[A], Proceedings of IJCAI Workshop on Empirical Methods in Artificial Intelligence[C],2001:41-46
[14]周俊.铜闪速熔炼贫化电炉渣含铜的线性回归分析[J],矿冶,2003,12(2)： 58-62
[15]A K Jain, M N Murty, P J Flynn. Data clustering:A review[J], ACM Computing Surveys,1999,31(3):264-232
[16]P Giordani, H A L Kiers. A comparison of three methods for principal component analysis of fuzzy interval data[J], Computational Statistics& Data Analysis,2006, 51(1):379-397
[17]E Eskin. Anomaly Detection over Noisy Data using Learned Probability Distributions[A], Proceedings of the Seventeenth International Conference on Machine Learning[C],2000:255-262
[18]Judea Pearl. Fusion, propagation, and structuring in belief networks[J], Artificial Intelligence,1986,29(3):241-288.
[19]G F Cooper, C Yoo. Causal discovery from a mixture of experimental and observational data[A], Fifth Annual Conference on Uncertainty in Artificial Intelligence[C], Morgan Kaufmann,1999:116-125
[20]S Tong, D Koller. Active learning for structure in Bayesian networks[A]. Procceeding of the International Joint Conference on Artificial Intelligence[C]. Scattle, Washington:Morgan Kaufinann,2001:863-869
[21]K Murphy. Active learning of causal Bayes net structure[R]. Berkeley: University of California,2001
[22]J Tian, J Pearl. Causal discovery from changes[A], Seventeenth Annual Conference on Uncertainty in Artificial Intelligence[C], Washington,2001:512-521
[23]邢杰,萧德云.基于PCA的概率神经网络结构优化[J],清华大学学报(自然科学版),2008,48(1)：141-144
[24]林亚平,杨小林.快速概率分析进化算法及其性能研究[J],电子学报,2001,29(2)：178-181
[25]焦李成.神经网络系统理论[M],西安：西安电子科技大学出版社,1996
[26]M W Craven, J W Shavlik. Using Neural Networks for Data Mining[J], Future Generation Computer Systems,1998:345-350
[27]S Hawkins, H X He, G Williams. Outlier Detection Using Replicator Neural Networks[A]. Proceedings of the Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK02) [C], London,2002:170-180
[28]王宪保,周德龙,王守觉.基于仿生模式识别的构造型神经网络分类方法[J],计算机学报,2007,30(12)：2109-2114
[29]雷景生,马军,靳婷.基于分级神经网络的Web文档模糊聚类技术[J],计 算机研究与发展,2006,43(10)：1695-1699
[30]马君,刘小冬,孟颖.基于神经网络的城市交通流预测研究[J],电子学报,2009,37(5)：1092-1094
[31]S Mitra, K P Sankar, P Mitra. Data Mining in Soft Computing Framework:A Survey[J], IEEE Transactions on neural networks,2002,13(1):3-14
[32]U Anders, O Korn. Model selection in neural networks[J], Neural Networks, 1999,12(2):309-323
[33]S E Fahlman, C Lebiere. The cascade correlation learning architecture[A], Advances in Neural Information Processing Systems[C]. San Mateo, USA,1990
[34]M C Mozer, P Smolensky. Skeletonization:A technique for trimming the fat from a network via relevance assessment [A]. Advances in neural information processing(1) [C], Denver,1989:107-115
[35]Z Cataltepe, Y S Abumostafa, M Magdon-Ismail. No free lunch for early stopping[J], Neural Computation,1999,11(4):995-1009
[36]F D Foresee, M T Hagan. Gauss-Newton approximation to Bayesian reguiarization[C], Proceedings of the 1997 international joint conference on neural network,1997:1930-1935
[37]S I Gallant. Connectionist expert systems[J], Communications of the ACM,1988, 31(2):152-69
[38]A B Tickle, R Andrews, M Golea, etc. The truth will come to light:Directions and challenges in extracting the knowledge embedded within trained artificial neural networks[J], IEEE Transactions on Neural Networks,1998,9(1):1057-1068
[39]R Setiono. Extracting M-of-N rules from trained neural networks[J], IEEE Transactions on Neural Networks,2000,11(2):512-519
[40]何新贵.模糊知识处理的理论与技术[M],北京：国防工业出版社,1994
[41]J S R Jang. ANFIS:adaptive-network-based fuzzy inference system[J], IEEE Transaction Systems, Man, and Cybernetics,1993,23 (3):665-685
[42]L Yu, J Xiao. Trade-off between accuracy and interpretability:Experience oriented fuzzy modeling via reduced-set vectors[J], Computers and Mathematics with Applications,2009,57(6):885-895
[43]T A Johansen, R Shorten, R Murray-Smith. On the Interpretation and Identification of Dynamic Takagi-Sugeno Fuzzy Models[J], IEEE Transactions on Fuzzy Systems,2000,8(3):297-213
[44]Y Li, J M Deng, M Y Wei, Meaning and precision of adaptive fuzzy systems with Gaussian-type membership functions[J], Fuzzy Sets and Systems,2002,127(1): 85-97
[45]S M Zhou, J Q Gan. Low-level interpretability and high-level interpretability:a unified view of data-driven interpretable fuzzy system modeling[J], Fuzzy Sets and Systems,2008,159(23):3091-3131
[46]E Hullermeier. Fuzzy methods in machine learning and data mining:Status and prospects[J], Fuzzy Sets and Systems,2005,156(3):387-406
[47]E Hullermeier. Fuzzy sets in machine learning and data mining[J], Applied Soft Computing. (2008), doi:10.1016/j.asoc.2008.01.004
[48]张学工.关于统计学习理论与支持向量机[J],自动化学报,2000,26(1)：32-42
[49]B E Boser, I M Guyon, V N Vapnik. A training algorithm for optimal margin classifiers[C], Proceedings of the fifth annual workshop on Computational learning theory, USA,1992:144-152
[50]S Amari, S Wu. Improving support vector machine classifiers by modifying kernel functions[J], Neural Networks,1999,12 (6):783-789
[51]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M],北京：科学出版社，2004
[52]Z Pawlak. Rough sets[J], International Journal of Information and Computer Science,1982,11(5):341-356
[53]张文修,吴伟志.粗糙集理论介绍和研究综述[J],模糊系统与数学,2000,14(4)：1-11
[54]J G Bazan, M S Szczuka, A Wojna, etc. On the Evolution of Rough Set Exploration System[A], Rough Sets and Current Trends in Computing[C],2004: 592-601
[55]J G Bazan, M S Szczuka, J Wroblewski. A New Version of Rough Set Exploration System[A], Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing[C],2002:397-404
[56]曾黄麟.智能计算——关于粗集理论、模糊逻辑、神经网络的理论及其应用[M],重庆：重庆大学出版社,2004
[57]J W Grzymala-Busse. LERS:a system of learning for examples based on rough sets[Ch], Intelligent Decisions Support:Handbook of applications and advances of rough sets theory,1992, Kluwer Academic Publishers, Boston:3-18
[58]J W Grzymala-Busse. A New Version of the Rule Induction System LERS[J], Fundamenta Informaticae,1997,31(1):27-39
[59]张曼,吴涛,王容川,等.基于粗糙集理论和构造型神经网络的分类算法研究[J],模式识别与人工智能,2004,17(3)：362-367
[60]黎明,张化光.基于粗糙集的神经网络建模方法研究[J],自动化学报,2002,28(1)：27-33
[61]张雪峰,田晓东,张庆灵.基于粗糙集理论和层次分析的数据约简[J],东北大学学报(自然科学版),2008,29(01)：21-24
[62]S T Hsieh, T Y Sun, C C Liu. Potential offspring production strategies:An improved genetic algorithm for global numerical optimization[J], Expert Systems with Applications,2009,36(8):11088-11098
[63]P K Tripathi, S Bandyopadhyay, S K Pal. Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients[J], Information Sciences,2007,177(22):5033-5049
[64]B Naderi, M Khalili, R Tavakkoli-Moghaddam. A hybrid artificial immune algorithm for a realistic variant of job shops to minimize the total completion time[J], Computers& Industrial Engineering,2009,56(4):1494-1501
[65]D X Yang, G Li, G D Cheng. On the efficiency of chaos optimization algorithms for global optimization Chaos[J], Solitons& Fractals,2007,34(4):1366-1375
[66]N Jiang, Z Y Zhao, L Q Ren. Design of structural modular neural networks with genetic algorithm[J], Advances in Engineering Software,2003,34(1):17-24
[67]卢荣德,陈宗海,王雷.思维模拟在复杂工业过程建模中应用的综述[J],信息与控制.2002,3 1(4)：346-352
[68]王凌,王雄,金以慧.MES——流程工业CIMS发展的关键[J],化工自动化仪表,2001,28(4)：1-5
[69]胡志坤,桂卫华,彭小奇,等.过程数据挖掘及进展[J],控制工程,2004,11(S)：1-4
[70]胡志坤,桂卫华,彭小奇,等.有色金属冶金过程数据挖掘[J],有色金属,2003,33(2)：1-4
[7l]刘洪霖.化工冶金过程人工智能优化[M],北京：冶金工业出版社,1999
[72]F R A Jorgensen, B J Eliot. Flash furnace reaction shaft evaluation through simulation[C]. AUSIMM International conference on Extractive Metal of Gold and base Metals,1992:387-389
[73]梅炽,王前普,彭小奇,等.有色冶金炉窑的仿真与优化[J],中国有色金属学报,1996,6(4)：19-23.
[74]S Goto. Equilibrium calculations between matte, slag and gaseous phases in copper smelting[C], Copper Metallurgy Practice and Theory,1974:23-34
[75]S Goto. The Application of Thermodynamic Calculations to Converter Practice[C], Copper and Nickel Converters(New Orleans),1979:33-55
[76]P Tan, C Zhang. Computer model of copper smelting process and distribution behaviors of accessory elements[J], Journal of Central South University of Technology,1997.4(1):36-41.
[77]M Nagamori, P J Mackey. Thermodynamics of copper matte converting-part I: Fundamentals of the Noranda process[J], Metallurgy Transactions,1978,9(2): 255-271
[78]M Nagamori, W J Errington, P J Mackey, etc. Thermodynamic simulation model of the isasmelt process for copper matte[J], Metallurgical and materials transactions, 1994,25(6):839-853
[79]G Eriksson. Thermodynamic studies of high temperature equilibria. III. SOLGAS, a computer program for calculating the composition and heat condition of an equilibrium mixture[J], Acta Chem Scand,1971,25(7):2651-2657
[80]G Eriksson,. Thermodynamic studies of high temperature equilibria. XII. SOLGASMIX, a computer program for calculation of equilibrium compositions in multiphase systems[J], Chem Scr,1975,8(3):100-103
[81]B Bjorkman, G Eriksson. Quantitative Equilibrium Calculations on Conventional Copper Smelting and Converting[J], Canadian Metallurgical Quarterly,1982,21(4): 329-337
[82]R P Cherian, L N Smith, P S Midha. A neural network approach for selection of powder metallurgy materials and process parameters[J], Artificial Intelligence in Engineering,2000.14(1):39-44
[83]R Cass, B Radl. Adaptive process optimization using functional-link networks and evolutionary optimization[J], Control Engineering Practice,1996,4(11): 1579-1584
[84]R Parra, F Kongoli, R Parada. New approach for the optimization of copper concentrates flash combustion through the control of the blends and slag composition[A], Paper presented at:Modeling, Control, and Optimization in Ferrous and Non-Ferrous Industry as held at the Materials Science& Technology 2003 Conference[C], Chicago, USA,2003:107-115
[85]王玉曾,张英林,李青茹.有色冶金窑炉中的神经网络过程控制[J],兰州 大学学报(自然科学版),1998.34(4)：69-72
[86]万维汉,杨金义.镍闪速熔炼过程的模糊动态质量模型与控制[J],西安交通大学学报,2000.34(3)：54-59
[87]万维汉,袁永发.镍闪速熔炼过程的模糊建模[J],冶金自动化,2000,24(2)：9-12
[88]曾青云,周立,汪金良.基于自适应模糊神经网络的铜闪速熔炼冰铜温度模型研究[J],有色金属(冶炼部分),2007(2)：2-4,9
[89]曾青云,汪金良.铜闪速熔炼神经网络模型的建立[J],南方冶金学院学报,2003,24(5)：15-18
[90]彭小奇.镍熔炼节能降耗、智能决策技术和熔炼车间计算机信息集成网络的开发与应用[D],中南大学,长沙,1998
[91]胡燕瑜,桂卫华,李勇刚,等.基于BP神经网络的熔融锌液流量检测[J],有色金属,2003,55(3)：143-146.
[92]C Yang, G Deconinck, W Gui, etc. An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity[J], IEEE Transactions on Neural Networks,2002,13(1):229-236.
[93]陈晓方,桂卫华,蔡自兴,等.过程控制中的智能集成建模方法[J],系统仿真学报,2001,13(s)：8-11
[94]王雅琳,桂卫华.自适应监督式分布神经网络及其工业应用[J],控制与决策,2001,16(5)：549-552
[95]杜玉晓,吴敏,岑丽辉,等.铅锌烧结过程的集成建模方法及智能优化算法[J],中国有色金属学报,2004,25(8)：1458-1463
[96]杜玉晓,吴敏,桂卫华.铅锌烧结过程的智能集成优化控制系统[J],中南工业大学学报(自然科学版),2003,34(4)：345-349
[97]杜玉晓,吴敏,桂卫华.铅锌烧结过程智能集成优化控制技术[J],控制与决策,2004,19(10)：1091-1096
[98]中国有色金属科技信息网.智能集成优化控制技术及其在锌电解和炼焦配煤过程中的应用[EB/OL],中国有色金属工业技术开发交流中心,2007.10
[99]李劫,王前普,肖劲,等.预焙铝电解槽智能模糊控制系统[J],中国有色金属学报,1998，8(3)：557-562
[100]边友康,刘钢,丁凤其,等.大型预焙铝电解槽现代工艺技术条件的选择与实现[J],轻金属,2000(11)：34-38
[101]肖劲,张泰山,刘业翔,等.铝电解槽点式下料的专家模糊控制方法[J],中南工业大学学报(自然科学版),1998,29(1)：32-35
[102]彭小奇,梅炽,周孑民,等.多变量模糊控制模型辨识方法及其在矿热电炉决策支持系统中的应用[J],控制理论与应用,1994,11(5)：582-587
[103]彭小奇,梅炽,周孑民,等.多变量系统的模糊神经网络控制模型及其应用[J],控制理论与应用,1995.12(3)：351-357
[104]R Sutinen, Causticizing plant and lime kiln computer control[J], Pulp and Paper, 1981,82(8):90-95
[105]高玉琦,李友善.水泥回转窑的计算机控制[J],自动化学报,1991,17(2)：166-173
[1061燕延,周彦萍.回转窑模糊控制系统[J],河北省科学院学报,1999,16(1)：32-34
[107]姚维,诸静.水泥回转窑生产过程的模糊控制[J],化工自动化及仪表,2000,27(2): 15-18
[108]章兢.回转窑集成智能控制系统[J],电工技术学报,2002,17(4)：62-66
[109]A Mrozek, Z Pawlak. Use of rough sets and decision tables for implementing rule-based control of industrial processes[J], Bulletin of the Polish Academy of Sciences,1986,34(5-6):357-371
[110]高赞,侯嫒彬.基于粗糙集理论的控制规则提取方法[J],工矿自动化,2003(4)：12-14
[111]张晖,吴斌,刘知贵.基于粗糙集理论的控制规则生成方法[J],计算机工程与应用,2003.39(13)：98-100
[112]常晓艳,刘振娟.基于粗糙集属性约简的过程控制规则提取[J],仪器仪表学报,2004，25(z1)：881-883
[113]崔庆安.基于粗糙集的焦炉加热工序控制规则提取[J],化工自动化及仪表,2005,32(6)：20-23
[114]黎明,张化光,何希勤.基于粗糙集的模糊模型辨识方法[J],东北大学学报(自然科学版),2000,21(5)：480-483
[115]刘璨,陈统坚,彭永红,等.基于粗集理论的模糊神经网络建模方法研究[J],中国机械工程,2001,12(11)：1256-1259
[116]陈双叶,易继锴.基于粗糊集理论的模糊模型及其在复杂控制系统中的应用[J],控制与决策,2002,17(B11)：747-751
[117]袁兵,江丽,朱宏辉.基于粗糙集理论的模糊控制规则的获取方法[J],武汉理工大学学报(交通科学与工程版),2005(3)：357-371
[118]陈泽华,谢克明.利用粗糙集获取模糊控制规则[J],太原理工大学学报,2003,34(3)：258-260
[119]黄金杰,李士勇,左兴权.一种T-S型粗糙模糊控制器的设计与仿真[J],系统仿真学报,2004,16(3)：480-484
[120]王介生,战红仁,王伟.基于粗糙集的TS模糊神经网络在回转窑烧结过程中的应用[J],华东理工大学学报(自然科学版),2006,32(007)：796-800
[121]C G Knickerbocker, R L Moore, L B Hawkinson, etc. The PICON expert system for process control[A], the 5th international workshop[C], Avignon, Agence de l'Informatique Paris La Defense, France,1986:59-66
[122]M Gallanti, L Tomada, R Tarli. Intelligent on-line system for cycle chemistry diagnostics in power plants[A], Proceedings of the American Power Conference[C], Chicago, IL, United States,1990:356-362
[123]B Porter, A H Jones, C B McKeown. Real-time expert tuners for PI controllers[A], IEE Proceedings D. Control Theory and Applications[C],1987: 260-263
[124]张学东,寇晓军.水泥回转窑实时专家控制系统的研制[J],中国矿业,2000,9(3)：82-84
[125]吴敏,唐朝晖.锌湿法冶炼电解过程的神经网络专家控制[J],自动化学报,2001,27(6)：867-869
[126]刘晓颖,桂卫华.铅锌冶炼过程的故障诊断神经网络专家系统[J],上海海运学院学报,2001,22(003)：89-91
[127]邓宏贵,罗安,丁家峰,等.递阶智能控制在电弧炉控制系统中的应用[J],电力系统及其自动化学报,2003,15(004)：51-54
[128]王春林,周昊,李国能,等.大型电厂锅炉NO_x排放特性的支持向量机模型[J], 浙江大学学报(工学版),2006,40(10)：1787-1791
[129]李丽娜,侯朝桢.基于支持向量机(SVM)的工业过程辨识[J].北京理工大学学报,2003,23(5)：569-571
[130]张立岩,岳恒,张君,等.基于最小二乘—支持向量机的制粉过程煤粉细度软测量模型[J].清华大学学报(自然科学版),2007,47(S2)：1932-1935
[131]张朴,万磊,孔力,等.基于支持向量机的激光焊接过程质量监测[J],华中科技大学学报(自然科学版),2007,35(7)：62-63
[132]F Torre, H P Wynn, P Corbett, etc. Data mining issues on modern multivariate online industrial process control[A], Paper presented at:7th Scandinavian Symposium on Chemometrics[C], Copenhagen, Denmark, John Wiley& Sons, Inc.,2001
[133]H Mori. State-of-the-Art Overview on Data Mining in Power Systems[A],2006 Power Systems Conference and Exposition, PSCE'06.2006 IEEE PES[C], Atlanta, GA,2006:33-34.
[134]F V D Berg. SSC7:7th Scandinavian Symposium on Chemometrics[EB/OL]. John Wiley& Sons, Inc.
[135]P Butler.过程控制的革命性剧变：知识系统提高用户的经营业绩[J],数字石油和化工,2006,1：71-72
[136]倪建军,马小平,王耀才.数据挖掘技术在工业控制系统中的应用研究[J],工业控制计算机,2004,17(3)：5-5,8
[137]朱群雄,麻德贤.过程工业新热点——数据挖掘[J],数字化工,2003(10)：38-39
[138]王耀南,宋明.复杂工业系统的广义知识模型与智能建模[J],中南工业大学学报,2003,34(4)：335-341
[139]刘业翔,陈湘涛,张更容,等.铝电解控制中灰关联规则挖掘算法的应用[J],中国有色金属学报,2004,14(3)：494-498
[140]铁军,朱旺喜.数据挖掘技术在铝电解生产中的应用[J],有色金属,2003,55(1): 56-59
[141]马元元,张南翔.增量关联规则在大型火力发电厂实时控制中的应用[J],工业控制计算机,2000,13(1)：14-15
[142]谭光兴,杜启亮,毛宗源.基于克隆选择的锌钡白煅烧过程数据挖掘[J],计算机工程与应用,2006,42(35)：211-213,218
[143]胡志坤.复杂有色金属熔炼过程操作模式智能优化方法[D],中南大学,长沙，2005
[144]胡志坤,桂卫华,彭小奇.有色冶金过程的数据挖掘[J],有色金属,2003,55(2)：47-50.
[145]宋彦坡.数据挖掘技术及其在铜转炉吹炼过程优化中的应用[D],中南大学,长沙,2005
[146]宋彦坡,彭小奇.数据挖掘技术及其在工业生产中的应用[J],计算机测量与控制,2004,12(10)：944-947
[147]卢勇,徐向东.数据挖掘与锅炉负荷多模型自适应控制研究[J],电站系统工程,2003,19(4)：49-51
[148]卢勇,徐向东,数据挖掘技术在热电厂过程控制与优化中的应用研究[J],电站系统工程,2003,19(2)：48-50
[149]李建强,牛成林,刘吉臻.数据挖掘技术在火电厂优化运行中的应用[J],动力工程,2006,26(6)：830-835
[150]吴少敏,陈贻龙.实用数据挖掘系统[J],冶金自动化,2002,26(1)：6- 10
[151]吴少敏,冯建生.宝钢数据挖掘系统[J],宝钢技术,2001(1)：43-47.
[152]吴少敏,冯建生.数据挖掘技术及其应用[J],冶金自动化,2001,25(6)：5-8
[153]高俊.模糊控制查询表的数据挖掘研究[J],计算机工程与应用,2004,40(33)：209-211
[154]赵妍,逢玉俊,文东丽.从样本数据中提取模糊规则的算法研究[J],石油化工高等学校学报,2004,17(3)：83-88
[155]丁彦明,庞维诚.数据库知识发现技术在连铸生产中的应用探索[J],铸造设备研究,2002(1)：13-15
[156]杨杰,叶晨洲.用于建模、优化、故障诊断的数据挖掘技术[J],计算机集成制造系统,2000,6(5)：72-76
[157]杨学瑜,宋晓娟,顾合英.数据挖掘在选煤厂过程控制中的应用[J],煤炭科学技术,2004,32(5)：21-24
[158]吴以凡,艾丽君,欧阳树生,等.面向钢铁生产过程质量控制的动态数据挖掘方法[J],冶金自动化,2006,30(4)：6-10
[159]邬成新.SAS数据挖掘在钢铁终轧温度控制中的应用[J],控制工程(沈阳),2005,12(6)：590-592
[160]张运陶,杨晓丽.轻烃回收装置数据挖掘及生产优化[J],计算机与应用化学,2005,22(7)：555-560
[161]刘敦楠,何光宇,范旻,等.数据挖掘与非正常日的负荷预测[J],电力系统自动化,2004,28(3)：53-57
[162]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J],电力系统自动化,2004,28(17)：l-11
[163]KDnuggets. Polls:Data preparation part in data mining projects,2003.10, http://www.kdnuggets.com/polls/2003/data_preparation.htm
[164]朱祖泽,马克毅.铜冶金学[M],昆明：云南科技出版社,1995
[165]任鸿久,王立川.有色金属提取冶金手册·铜镍卷[M],北京：冶金工业出版社,2000
[166]毕诗文,于海燕,杨毅宏,等.拜耳法生产氧化铝[M],北京：冶金工业出版社,2007
[167]毕诗文.氧化铝生产工艺[M],北京：化学工业出版社,2006
[168]梅炽.冶金传递过程原理[M],长沙：中南工业大学出版社,1987
[169]A Famili, W M Shen, R Weber, etc. Data Preprocessing and Intelligent Data Analysis[J], Intelligent Data Analysis,1997,1(1):3-23
[170]E Rahm, H D Hong. Data cleaning:problems and current approaches[J], IEEE Data Engineering Bulletin,2000,23(4):3-13
[171]颜钰芬,徐明钧.数理统计[M],上海：上海交通大学出版社,1992
[172]汪荣鑫.数理统计[M],西安：西安交通大学出版社,1986
[173]M R Chmielewski, J W Grzymala-Busse. Global Discretization of Continuous Attributes as Preprocessing for Machine Learning[J], International Journal of Approximate Reasoning,1996:320-331
[174]A Madkiewicz, W Ratajczak. Principal components analysis (PCA)[J], Computers& Geosciences,1993,19(3):303-342
[175]S Wold, J Trygg, A Berglund, etc. Some recent developments in PLS modeling[J], Chemometrics and Intelligent Laboratory Systems,2001,58(2): 131-150
[176]J W Sammon. A Nonlinear Mapping for Data Structure Analysis[J], IEEE Transactions on Computers,1969,18(5):401-409
[177]D Hawkins. Identification of Outliers[M], London:Chapman and Hall,1980
[178]E Eskin. Anomaly Detection over Noisy Data using Learned Probability Distributions[A], Proc.17th International Conf. on Machine Learning[C], San Francisco, CA, USA,2000:255-262
[179]E M Knorr, R T Ng. Algorithms for mining distance-based outliers in large datasets[A], Proceedings of the 24th International Conference on Very Large Data Bases[C], New York:Morgan Kaufmann,1998:392-403
[180]E M Knorr, R T Ng. Finding intensional knowledge of distance-based outliers[A], Proceedings of the 25th International Conference on Very Large Data Bases[C], Edinburgh, Scotland:Morgan Kaufmann,1999:211-222
[181]M M Breunig, H P Kriegel, R T Ng, etc. LOF:identifying density-based local outliers[A], Proceedings of the ACM SIGMOD International Conference on Management of Data[C], Dallas, Texas:ACM Press,2000:93-104
[182]M M Breunig, H P Kriegel, R T Ng, etc. OPTICS-OF:identifying local outliers[A], Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases[C], Lecture Notes in Computer Science (LNAI 1704), Prague:Springer,1999:262-270
[183]M F Jiang, S S Tseng, C M Su. Two-phase clustering process for outliers detection[J], Pattern Recognition Letters,2001,22(6-7):691-700
[184]S Hawkins, H X He, G Williams, etc. Outlier Detection Using Replicator Neural Networks[J], Proceedings of the Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK02), London, UK,2002:170-180
[185]A Arning, R Agrawal, P Raghavan. A linear method for deviation in large database[J], Conf. Data Mining and Knowledge Discovery(KDD96), Portland,1996: 164-169
[186]H G Stark. Continuous wavelet transform and continuous multiscale analysis[J], Journal of Mathematical Analysis and Applications,1992,169(1):179-196
[187]杨福生.小波变换的工程分析与应用[M],北京：科学出版社,2000
[188]B George, N R Draper. Empirical Model-Building and Response Surfaces[M], New York:Wiley,1987
[189]A Ashok. Analog and digital signal processing(Second Edition)[M], BROOKS/ COLE publishing company,1999
[190]L Devroye, L Gyorfi, D Schafer, etc. The estimation problem of minimum mean squared error[J], Statistics and Decisions,2003,21:15-28
[191]M J Buckley, G K Eagleson. The estimation of residual variance in nonparametric regression[J], Biometrika,1988,75(2):189-199
[192]M H Radfar, R M Dansereau, A Sayadiyan. Nonlinear minimum mean square error estimator for mixture-maximisation approximation[J], Electronics Letters,2006, 42(12):724-725
[193]J Immerkaer. Fast noise variance estimation[J], Computer Vision and Image Understanding,1996,64(2):300-302
[194]V Vapnik, A Chervonenkis. Necessary and sufficient conditions for the uniform convergence of means to their expectations[J], Theory of Probability and its Applications,1981,26(3):532-553
[195]K M Hornik, M Stinchcombe. Multilayer feed-forward networks are universal approximators[J]. Neural Networks,1989,2:359-366
[196]Y L Cun, J S Denker, S A Solla. Optimal brain damage[A]. Advances in neural information processing (2) [C], Denver,1990:598-605
[197]P F Konstantinos. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms[J]. Neural Networks, 2005,18(7):934-950
[198]S Haralambos, A Alex, M Stefanos, etc. A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms[J]. Computers& Chemical Engineering,2004,28(1-2):209-217
[199]J B Yu, S Jn Wang, L F Xi. Evolving artificial neural networks using an improved PSO and DPSO[J], Neurocomputing,2008,71(4-6):1054-1060
[200]Y Chauvin. A back-propagation algorithm with optimal use of hidden units[A]. Advances in neural information processing(2) [C], Denver,1990:642-649
[201]W L Buntine, A S Weigend. Bayesian back-propagation[J]. Complex Systems, 1991,5:603-643
[202]V Vapnik. The nature of statistical learning theory[M], Springer Verlag,1995
[203]N Cristianini, J Kandola, A Elisseeff, etc. On kernel target alignment[J], Advances in Neural Information Processing Systems,2002,14:367-373
[204]J Kandola, J Shawe-Taylor, N Cristianini. On the Extensions of Kernel Alignment[R],2002, http://www.neurocolt.org
[205]A J Smola, N Murata, B Scholkopf, etc. Asymptotically optimal choice of epsion-loss for support vector machines[A], Proceedings of the 8th International Conference on Artificial Neural Networks[C], Skovde, Sweden,1998,1:105-110
[206]J T Kwok, I W Tsang. Linear dependency between E and the input noise in E-support vector regression[J], IEEE Transactions on Neural Networks,2003,14(3): 544-553
[207]V Cherkassky, Y Q Ma. Practical selection of SVM parameters and noise estimation for SVM regression[J], Neural Networks,2004,17(1):113-126
[208]D Mattera, S Haykin. Support vector machines for dynamic reconstruction of a chaotic system[Ch], in:B. Scholkopf, J. Burges, A. Smola, ed., Advances in Kernel Methods:Support Vector Machine[M], MIT Press,1999
[209]江铜贵溪冶炼厂.内部培训教材·熔炼车间·转炉[M]，贵溪：贵溪冶炼厂,2000
[210]李文超.冶金与材料物理化学[M],北京：冶金工业出版社,2001
[211]周土平,石东坡,陈春林,等.贵冶铜转炉工业实验及物质平衡数据分析[J],有色金属(冶炼部分),1998,19(3)：7-9,18