基于改进孪生支持向量机的热电厂脱硫系统pH值预测
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摘要
在热电厂脱硫过程中,pH值直接影响脱硫的效率,若pH测量仪器受到环境的影响被破坏,会给生产造成巨大的损失。为了降低这种损失,采用改进的孪生支持向量机回归模型对pH值进行预测,首先将粒子群算法的权值和学习因子进行改进,然后用改进之后的粒子群算法对孪生支持向量机回归模型的惩罚参数和核函数的参数等进行寻优,再将最优的参数代入孪生支持向量机预测模型中,最后用MATLAB工具箱对pH值历史数据进行仿真,并与未改进的孪生支持向量机和BP神经网络预测技术进行比较。结果表明:该方法对脱硫系统中pH值的预测精度高,平均相对误差比未改进的孪生支持向量机和BP神经网络的预测结果小,能够显著改善脱硫装置的效率。
In the desulfurization process of thermal power plants, the pH value directly affects the efficiency of desulfurization. If the pH measuring instrument is damaged by the environment, it will cause huge losses to the production. In order to reduce this loss, the improved twin support vector machine regression model is used to predict the pH value. Firstly, the weight and learning factor of the particle swarm optimization algorithm are improved, and then the improved particle swarm optimization algorithm is used to support the support vector machine regression model. The penalty parameters and kernel function parameters are optimized, and then the optimal parameters are substituted into the twin support vector machine prediction model. Finally, the MATLAB toolbox is used to simulate the pH value historical data, and the unmodified twin support vector machine and BP neural network prediction techniques are compared. The results show that the method has high prediction accuracy for the pH value in the desulfurization system, and the average relative error is smaller than that of the unmodified twin support vector machine and BP neural network, which can significantly improve the efficiency of the desulfurization device.
In the desulfurization process of thermal power plants, the pH value directly affects the efficiency of desulfurization. If the pH measuring instrument is damaged by the environment, it will cause huge losses to the production. In order to reduce this loss, the improved twin support vector machine regression model is used to predict the pH value. Firstly, the weight and learning factor of the particle swarm optimization algorithm are improved, and then the improved particle swarm optimization algorithm is used to support the support vector machine regression model. The penalty parameters and kernel function parameters are optimized, and then the optimal parameters are substituted into the twin support vector machine prediction model. Finally, the MATLAB toolbox is used to simulate the pH value historical data, and the unmodified twin support vector machine and BP neural network prediction techniques are compared. The results show that the method has high prediction accuracy for the pH value in the desulfurization system, and the average relative error is smaller than that of the unmodified twin support vector machine and BP neural network, which can significantly improve the efficiency of the desulfurization device.
引文
[1] 王岩,朱齐丹,刘志林,等.改进的稀疏孪生支持向量回归算法[J].系统工程与电子技术,2012,34(9):1940-1945.WANG Yan,ZHU Qidan,LIU Zhilin,et al.Improved sparse twin support vector regression algorithm[J].Systems Engineering and Electronics,2012,34(9):1940-1945.
[2] PENG X.TSVR:An efficient twin support vector machine for regression[J].Neural Networks,2010,23(3):365-372.
[3] TOMAR D,PRASAD B R,AGARWAL S.An efficient Parkinson disease diagnosis system based on least squares twin support vector machine and particle swarm optimization[C]//IEEE International Conference on Industrial and Information Systems,2015:1-6.
[4] ZHAI S,PAN J,LUO H,et al.A new sense-through-foliage target recognition method based on hybrid particle swarm optimization-based wavelet twin support vector machine[J].Measurement,2016,80:58-70.
[5] QI Z,TIAN Y,YONG S.Robust twin support vector machine for pattern classification[J].Pattern Recognition,2013,46(1):305-316.
[6] 王方红,黄文彪.孪生支持向量机的特征选择研究[J].浙江工业大学学报,2016,44(2):146-149.WANG Fanghong,HUANG Wenbiao.Research on feature selection of twin support vector machine[J].Journal of Zhejiang University of Technology,2016,44(2):146-149.
[7] 陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416.CHEN Sugen,WU Xiaojun.Improved projection support vector machine[J].Chinese Journal of Electronics,2017,45(2):408-416.
[8] 许凤华,李述山.基于改进的偏最小二乘回归的酸雨pH值预测[J].山东科技大学学报(自然科学版),2006,25(3):110-112.XU Fenghua,LI Shushan.pH prediction of acid rain based on improved partial least squares regression[J].Journal of Shandong University of Science and Technology(Natural Science Edition),2006,25(3):110-112.
[9] 储茂祥,王安娜,巩荣芬.一种改进的最小二乘孪生支持向量机分类算法[J].电子学报,2014,42(5):998-1003.CHU Maoxiang,WANG Anna,GONG Rongfen.An improved least squares twin support vector machine classification algorithm[J].Chinese Journal of Electronics,2014,42(5):998-1003.
[10] MEI D,XU E,QI Y.Short-term traffic flow prediction method based on grey adaptive particle swarm support vector machine[C]// IEEE International Conference on Computer and Communications,2017:1294-1298.
[11] BAMAKAN S M H,WANG H,RAVASAN A Z.Parameters optimization for nonparallel support vector machine by particle swarm optimization[J].Procedia Computer Science,2016,91:482-491.
[12] LI D,ZHANG H,KHAN M S,et al.A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition[J].Biomedical Signal Processing and Control,2018,41:222-232.
[13] CHEN S G,WU X J.A new fuzzy twin support vector machine for pattern classification[J].International Journal of Machine Learning and Cybernetics,2017(3):1-12.
[14] HANG S,QIANG L I,QIN Z,et al.Variational data assimilation based on parallel particle swarm optimization[J].Journal of PLA University of Science and Technology,2014,15(6):583-590.
[15] XU Y,YANG Z,PAN X.A novel twin support-vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
[2] PENG X.TSVR:An efficient twin support vector machine for regression[J].Neural Networks,2010,23(3):365-372.
[3] TOMAR D,PRASAD B R,AGARWAL S.An efficient Parkinson disease diagnosis system based on least squares twin support vector machine and particle swarm optimization[C]//IEEE International Conference on Industrial and Information Systems,2015:1-6.
[4] ZHAI S,PAN J,LUO H,et al.A new sense-through-foliage target recognition method based on hybrid particle swarm optimization-based wavelet twin support vector machine[J].Measurement,2016,80:58-70.
[5] QI Z,TIAN Y,YONG S.Robust twin support vector machine for pattern classification[J].Pattern Recognition,2013,46(1):305-316.
[6] 王方红,黄文彪.孪生支持向量机的特征选择研究[J].浙江工业大学学报,2016,44(2):146-149.WANG Fanghong,HUANG Wenbiao.Research on feature selection of twin support vector machine[J].Journal of Zhejiang University of Technology,2016,44(2):146-149.
[7] 陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416.CHEN Sugen,WU Xiaojun.Improved projection support vector machine[J].Chinese Journal of Electronics,2017,45(2):408-416.
[8] 许凤华,李述山.基于改进的偏最小二乘回归的酸雨pH值预测[J].山东科技大学学报(自然科学版),2006,25(3):110-112.XU Fenghua,LI Shushan.pH prediction of acid rain based on improved partial least squares regression[J].Journal of Shandong University of Science and Technology(Natural Science Edition),2006,25(3):110-112.
[9] 储茂祥,王安娜,巩荣芬.一种改进的最小二乘孪生支持向量机分类算法[J].电子学报,2014,42(5):998-1003.CHU Maoxiang,WANG Anna,GONG Rongfen.An improved least squares twin support vector machine classification algorithm[J].Chinese Journal of Electronics,2014,42(5):998-1003.
[10] MEI D,XU E,QI Y.Short-term traffic flow prediction method based on grey adaptive particle swarm support vector machine[C]// IEEE International Conference on Computer and Communications,2017:1294-1298.
[11] BAMAKAN S M H,WANG H,RAVASAN A Z.Parameters optimization for nonparallel support vector machine by particle swarm optimization[J].Procedia Computer Science,2016,91:482-491.
[12] LI D,ZHANG H,KHAN M S,et al.A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition[J].Biomedical Signal Processing and Control,2018,41:222-232.
[13] CHEN S G,WU X J.A new fuzzy twin support vector machine for pattern classification[J].International Journal of Machine Learning and Cybernetics,2017(3):1-12.
[14] HANG S,QIANG L I,QIN Z,et al.Variational data assimilation based on parallel particle swarm optimization[J].Journal of PLA University of Science and Technology,2014,15(6):583-590.
[15] XU Y,YANG Z,PAN X.A novel twin support-vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
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