智能优化支持向量机预测算法及应用研究
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摘要
机器学习是人工智能领域非常前沿的、极具智能特征的研究方向之一。支持向量机(Support Vector Machine,SVM)是由Vladimir Naumovich Vapnik等学者于1992年提出的一类新型机器学习方法,该方法以统计学习理论为理论体系,通过寻求结构风险最小化实现学习的真实风险最小化,追求在有限信息条件下得到最佳的学习效果,具有全局最优、结构简单、推广能力强等优点。在20世纪90年代中后期得到了全面深入的发展,在人脸检测、语音识别、图像处理、文本分类、邮件过滤等方面取得了大量的研究成果,成为继人工神经网络之后机器学习领域的又一研究热点。
支持向量机集成了最大间隔超平面、Mercer核、凸二次规划、稀疏解和松弛变量等多项技术,是机器学习领域多项标准技术的整合和提高,在许多极具挑战性的应用中获得了很好的效果。
但是,作为一类新兴的革命性的方法,支持向量机还需要进一步研究模型选择、参数优化、大样本快速训练、以及多类分类等问题。
本文将支持向量机的理论、方法和应用三者相结合,深入研究支持向量机模型选择、参数优化、快速训练等问题,提出了三种新的支持向量机预测算法。本文的主要工作和贡献如下:
1.提出基于混沌时间序列的粒子群优化支持向量机预测算法(CP-SVM)
传统时间序列预测方法通常需要对时间序列的特性进行简化,加入了时间序列线性及平稳性的假定,然而这一假定与实际获得的时间序列差别很大,导致了传统时间序列预测方法实际应用效果不佳。人工神经网络预测方法虽然能够在一定程度上进行非线性时间序列预测,但不能解决应用上的“过学习”及局部极小值等问题。本文分析了经典时间序列分析方法的不足,并且在研究时间序列混沌特性的基础上,提出了基于混沌时间序列的粒子群优化支持向量机预测算法(CP-SVM),该算法的创新点在于:结合时间序列的混沌特性,针对预测模型系数的时变特性,采用粒子群算法对模型参数进行寻优,得到最佳的模型参数,以达到提高预测精度的目的。本文采用该算法对电力负荷这类复杂的非线性非平稳时间序列进行短期预测,提高了预测精度。
2.提出基于核主成分分析的量子行为粒子群优化支持向量机预测算法(KQP-SVM)
CP-SVM预测算法可以得到较高的预测精度,但预测速度较慢。为了提高预测速度,本文提出了基于核主成分分析的量子行为粒子群优化支持向量机预测算法(KQP-SVM)。KQP-SVM算法的创新点在于:在研究时间序列混沌特性的基础上确定合理的延迟时间和嵌入维数,采用核主成分分析方法提取非线性时间序列的特征,并采用量子行为粒子群算法优化支持向量机核函数的参数。本文将KQP-SVM算法应用于电力负荷短期预测,结果表明,该算法在保证预测精度的前提下,明显地提高了预测速度。
3.提出改进序列最小优化算法训练样本的小波核最小二乘支持向量机预测算法(SWLS-SVM)
SWLS-SVM算法的创新点如下:
(1)对于更加复杂的时间序列,充分发掘小波分析提取信号高频细节特征方面的优势,构造小波核最小二乘支持向量机预测算法。该算法对于复杂度更高的混沌时间序列,可以得到更高的预测精度。
(2)为了弥补最小二乘支持向量机丧失稀疏性的不足,采用改进的序列最小优化算法对样本进行训练,提高训练速度。
4.将SWLS-SVM算法应用于火电锅炉主汽温预测控制系统,实现了典型热工系统的先进控制。实验数据表明,SWLS-SVM算法的预测效果优于人工神经网络和标准支持向量机等常用预测方法。
Machine Learning is one of the most advanced and intelligence-featured domains in Artificial Intelligence research. Support Vector Machine (SVM), as a new method of Machine Learning, was proposed by Vladimir Naumovich Vapnik and other scholars in 1992. Based on Statistics Learning Theory, the method seeks for optimal learning effect under limited information by actual risk minimization with structure risk minimization. Support Vector Machine has been regarded as one of new research hotspots following Artificial Neural Network.
Support Vector Machine integrates technologies in maximum interval hyper-plane, Mercer kernel, convex quadratic programming, sparse solution and slack variable and makes best effect in many challenging applications.
However, as a newly-rising, revolutionary technology, Support Vector Machine needs to solve some problems including model selection, parameter optimization, large sample fast training and multi-class classification. This dissertation combines the theory, method and application of Support Vector Machine together, makes a thorough study on the model selection, parameter optimization, and large sample fast training of SVM, then proposes three predict algorithms for SVM. The main work and contribution in this dissertation is as follows:
1. Proposition of Chaotic-based Particle swarm optimization Support Vector Machine (CP-SVM) Algorithm. Traditional prediction algorithm on time series makes the assumption of linearity and stationary of time series to simplify characteristic of time series. However, the assumption makes obvious difference from the practical time series, causing unsatisfactory result in practical application. Although Artificial Neural Network prediction algorithms can make certain prediction on non-linear time series it can not solve problems of over-fitting and local minimum. This dissertation proposes CP-SVM algorithm by analyzing the shortage of classical time series algorithms and characteristic of chaos time series. The novelty of this algorithms lies in that it seeks for optimal model parameter by Particle Swarm Optimization (PSO) algorithm according to the time-variation characteristics of prediction model coefficient. Such algorithm, applied in complicated non-linear, non-stationary time series as electricity load, improves precision for short-term prediction.
2. Proposition of Kernel principal component analysis based Quantum-behaved Particle swarm optimization algorithm of Support Vector Machines (KQP-SVM) Algorithm. The CP-SVM algorithm can acquire higher precision but with lower speed. To improve the prediction speed under permissible precision, this dissertation proposes KQP-SVM Algorithm. The novelties of KQP-SVM are as follows: it determines reasonable time series steps according to the chaotic characteristic of time series, gets characteristics of non-linear time series with kernel Principal Component Analysis, and optimizes parameter of SVM kernel function with Quantum-behaved Particle Swarm Optimization algorithm. The proposed method is then applied to short-term load prediction, and the results demonstrate that the algorithm can improve the prediction speed under permissible precision.
3. Proposition of Sequential minimal optimization Wavelet Least Square Support Vector Machine (SWLS-SVM) Algorithm. The novelties of SWLS-SVM are as follows: aiming at more complicated chaos time series, it takes advantages of Wavelet analysis on high frequency detail characteristics and the high speed of Least Squares Support Vector Machine, then constructs Wavelet Least Square Support Vector Machine prediction model. With this algorithm higher prediction precision can be acquired for more complicated chaos time series. Improved sequential minimal optimization algorithm is applied to samples to improve training speed and thus yields the sparse representation of Least Square Support Vector Machine.
4. Application of the SWLS-SVM algorithm. This algorithm is applied to main temperature control system of electric boiler, especially in advanced control of thermal system is accomplished typically. Experimental results show that SWLS-SVM algorithm has better performance than Artificial Neural Networks algorithm or standard Support Vector Machine algorithms in prediction.
支持向量机集成了最大间隔超平面、Mercer核、凸二次规划、稀疏解和松弛变量等多项技术,是机器学习领域多项标准技术的整合和提高,在许多极具挑战性的应用中获得了很好的效果。
但是,作为一类新兴的革命性的方法,支持向量机还需要进一步研究模型选择、参数优化、大样本快速训练、以及多类分类等问题。
本文将支持向量机的理论、方法和应用三者相结合,深入研究支持向量机模型选择、参数优化、快速训练等问题,提出了三种新的支持向量机预测算法。本文的主要工作和贡献如下:
1.提出基于混沌时间序列的粒子群优化支持向量机预测算法(CP-SVM)
传统时间序列预测方法通常需要对时间序列的特性进行简化,加入了时间序列线性及平稳性的假定,然而这一假定与实际获得的时间序列差别很大,导致了传统时间序列预测方法实际应用效果不佳。人工神经网络预测方法虽然能够在一定程度上进行非线性时间序列预测,但不能解决应用上的“过学习”及局部极小值等问题。本文分析了经典时间序列分析方法的不足,并且在研究时间序列混沌特性的基础上,提出了基于混沌时间序列的粒子群优化支持向量机预测算法(CP-SVM),该算法的创新点在于:结合时间序列的混沌特性,针对预测模型系数的时变特性,采用粒子群算法对模型参数进行寻优,得到最佳的模型参数,以达到提高预测精度的目的。本文采用该算法对电力负荷这类复杂的非线性非平稳时间序列进行短期预测,提高了预测精度。
2.提出基于核主成分分析的量子行为粒子群优化支持向量机预测算法(KQP-SVM)
CP-SVM预测算法可以得到较高的预测精度,但预测速度较慢。为了提高预测速度,本文提出了基于核主成分分析的量子行为粒子群优化支持向量机预测算法(KQP-SVM)。KQP-SVM算法的创新点在于:在研究时间序列混沌特性的基础上确定合理的延迟时间和嵌入维数,采用核主成分分析方法提取非线性时间序列的特征,并采用量子行为粒子群算法优化支持向量机核函数的参数。本文将KQP-SVM算法应用于电力负荷短期预测,结果表明,该算法在保证预测精度的前提下,明显地提高了预测速度。
3.提出改进序列最小优化算法训练样本的小波核最小二乘支持向量机预测算法(SWLS-SVM)
SWLS-SVM算法的创新点如下:
(1)对于更加复杂的时间序列,充分发掘小波分析提取信号高频细节特征方面的优势,构造小波核最小二乘支持向量机预测算法。该算法对于复杂度更高的混沌时间序列,可以得到更高的预测精度。
(2)为了弥补最小二乘支持向量机丧失稀疏性的不足,采用改进的序列最小优化算法对样本进行训练,提高训练速度。
4.将SWLS-SVM算法应用于火电锅炉主汽温预测控制系统,实现了典型热工系统的先进控制。实验数据表明,SWLS-SVM算法的预测效果优于人工神经网络和标准支持向量机等常用预测方法。
Machine Learning is one of the most advanced and intelligence-featured domains in Artificial Intelligence research. Support Vector Machine (SVM), as a new method of Machine Learning, was proposed by Vladimir Naumovich Vapnik and other scholars in 1992. Based on Statistics Learning Theory, the method seeks for optimal learning effect under limited information by actual risk minimization with structure risk minimization. Support Vector Machine has been regarded as one of new research hotspots following Artificial Neural Network.
Support Vector Machine integrates technologies in maximum interval hyper-plane, Mercer kernel, convex quadratic programming, sparse solution and slack variable and makes best effect in many challenging applications.
However, as a newly-rising, revolutionary technology, Support Vector Machine needs to solve some problems including model selection, parameter optimization, large sample fast training and multi-class classification. This dissertation combines the theory, method and application of Support Vector Machine together, makes a thorough study on the model selection, parameter optimization, and large sample fast training of SVM, then proposes three predict algorithms for SVM. The main work and contribution in this dissertation is as follows:
1. Proposition of Chaotic-based Particle swarm optimization Support Vector Machine (CP-SVM) Algorithm. Traditional prediction algorithm on time series makes the assumption of linearity and stationary of time series to simplify characteristic of time series. However, the assumption makes obvious difference from the practical time series, causing unsatisfactory result in practical application. Although Artificial Neural Network prediction algorithms can make certain prediction on non-linear time series it can not solve problems of over-fitting and local minimum. This dissertation proposes CP-SVM algorithm by analyzing the shortage of classical time series algorithms and characteristic of chaos time series. The novelty of this algorithms lies in that it seeks for optimal model parameter by Particle Swarm Optimization (PSO) algorithm according to the time-variation characteristics of prediction model coefficient. Such algorithm, applied in complicated non-linear, non-stationary time series as electricity load, improves precision for short-term prediction.
2. Proposition of Kernel principal component analysis based Quantum-behaved Particle swarm optimization algorithm of Support Vector Machines (KQP-SVM) Algorithm. The CP-SVM algorithm can acquire higher precision but with lower speed. To improve the prediction speed under permissible precision, this dissertation proposes KQP-SVM Algorithm. The novelties of KQP-SVM are as follows: it determines reasonable time series steps according to the chaotic characteristic of time series, gets characteristics of non-linear time series with kernel Principal Component Analysis, and optimizes parameter of SVM kernel function with Quantum-behaved Particle Swarm Optimization algorithm. The proposed method is then applied to short-term load prediction, and the results demonstrate that the algorithm can improve the prediction speed under permissible precision.
3. Proposition of Sequential minimal optimization Wavelet Least Square Support Vector Machine (SWLS-SVM) Algorithm. The novelties of SWLS-SVM are as follows: aiming at more complicated chaos time series, it takes advantages of Wavelet analysis on high frequency detail characteristics and the high speed of Least Squares Support Vector Machine, then constructs Wavelet Least Square Support Vector Machine prediction model. With this algorithm higher prediction precision can be acquired for more complicated chaos time series. Improved sequential minimal optimization algorithm is applied to samples to improve training speed and thus yields the sparse representation of Least Square Support Vector Machine.
4. Application of the SWLS-SVM algorithm. This algorithm is applied to main temperature control system of electric boiler, especially in advanced control of thermal system is accomplished typically. Experimental results show that SWLS-SVM algorithm has better performance than Artificial Neural Networks algorithm or standard Support Vector Machine algorithms in prediction.
引文
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