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化工学报  2015 

基于稀疏最小二乘支持向量机的软测量建模

DOI: 10.11949/j.issn.0438-1157.20141392, PP. 1402-1406

Keywords: 遗传算法,参数识别,整体优化,软测量,最小二乘支持向量机,4-CBA含量

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Abstract:

针对传统最小二乘支持向量机非稀疏化解问题,提出了基于遗传算法的最小二乘支持向量机稀疏化及参数优化方法,稀疏化的基本思想是给训练样本赋予一个概率值,将概率值小于0.5的样本作为测试样本,从而将总的训练样本集分成测试样本集和保留的训练样本集。定义了包括稀疏率、训练误差及测试误差在内的适应度函数。种群个体的前N维表示每个样本对应的概率,后m维表示要优化的参数。通过选择、交叉和变异操作对所有参数进行整体优化,取适应度最小的个体对应的保留的训练样本及优化参数建立最小二乘支持向量机模型。并用该方法用于PX氧化过程4-CBA含量的软测量中,工业数据仿真结果表明,用本文提出的方法稀疏化率达到87%,核参数选取自动完成,与稀疏前建立的模型相比推广能力更高。

References

[1]  Vapnik V, Levin E, Le Cun Y. Measuring the VC dimension of learning machines [J]. Neural Computation (S0899-7667),1994 (6): 851-876
[2]  Vapnik V. The Nature of Statistical Learning Theory [M]. New York: Springer, 1995
[3]  Gan Liangzhi (甘良志), Sun Zonghai (孙宗海), Sun Youxian (孙优贤). Sparse least squares vector machine [J]. Journal of Zhejiang University: Engineering (浙江大学学报: 工学版), 2007, 41 (2): 245-248
[4]  Chen Lei (陈磊).Genetic least squares support vector machine approach to hourly water consumption prediction [J].Journal of Zhejiang University:Engineering (浙江大学学报: 工学版), 2011, 45 (6): 1100-1103
[5]  Shang Wanfeng (尚万峰), Zhao Shengdun (赵升吨), Shen Yajing (申亚京). Application of LSSVM optimized by genetic algorithm to modeling of switched reluctance motor [J]. Proceedings of CSEE (中国电机工程学报), 2009, 29 (12): 65-69
[6]  Lin Bihua (林碧华), Gu Xingsheng (顾幸生). Soft sensor modeling based on DE-LSSWM [J].Journal of Chemical Industry and Engineering (China) (化工学报), 2008, 59 (7): 1681-1685
[7]  Vapnik V. Statistical Learning Theory [M]. New York: John Wiley, 1998
[8]  Cao Wei (曹巍), Zhao Yingkai (赵英凯), Gao Shiwei (高世伟). Multi-class support vector machines based on fuzzy kernel cluster [J].CIESC Journal (化工学报), 2010, 61 (2): 420-424
[9]  Wang Anna (王安娜), Li Yunlu (李云路), Zhao Fengyun (赵锋云), Shi Chenglong (史成龙). Novel semi-supervised classification algorithm based on TSVM [J]. Proceedings of CSEE (中国电机工程学报), 2011, 32 (7): 1546-1550
[10]  Yang Zhimin, He Junyun, Shao Yuanhai. Feature selection based on linear twin support vector machines [J]. Procedia Computer Science, 2013, 17: 1039-1046
[11]  Wendy Flores-Fuentes, Moises Rivas-Lopez, Oleg Sergiyenko, et al. Combined application of power spectrum centroid and support vector machines for measurement improvement in optical scanning systems [J]. Signal Processing, 2014, 98: 37-51
[12]  Wang Bo (王博), Sun Yukun (孙玉坤), Ji Xiaofu (嵇小辅), et al. Soft-sensor modeling for lysine fermentation processes based on PSO_SVM inversion [J]. CIESC Journal (化工学报), 2012, 63 (9): 3000-3007
[13]  Li Jin (李瑾), Liu Jinpeng (刘金朋), Wang Jianjun (王建军). Mid-long term load forecasting based on simulated annealing and SVM algorithm [J]. Proceedings of CSEE (中国电机工程学报), 2011, 31 (16): 63-66
[14]  Wang Zhanneng (王占能), Xu Zuhua (徐祖华), Zhao Jun (赵均), Shao Zhijiang (邵之江). Coal-fired power plant boiler combustion process modeling based on support vector machine and load data division [J]. CIESC Journal (化工学报), 2013, 64 (12): 4496-4502
[15]  Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J].Neural Processing Letters, 1999, 9 (3): 293-300
[16]  Suykens J A K, Lukas L, Vandewalle J. Sparse approximation using least squares support vector machine//IEEE Intenational Symposium on Circuits and Systems [C]. Geneva, Swizerland, 2000: 757-760
[17]  Suykens J A K, De Brabanter J, Lukas L, Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation [J]. Neurocomputing, 2002, 48: 85-105
[18]  Cawley Gavin C, Talbot Nicola L C. Improved sparse least-squares support vector machines [J]. Neurocomputing, 2002, 48: 1025-1031
[19]  Zhang Chunxiao (张春晓), Zhang Tao (张涛).Oil holdup modeling of oil-water two-phase flow using thermal method based on LSSVM and GA [J]. CIESC Journal (化工学报), 2009, 60 (7): 1651-1655
[20]  Li Xi (李希), Xie Gang (谢刚), Hua Weiqi (华卫琦). Key problems and research program for PTA process domestic development [J]. Polyester Industry (聚酯工业), 2001, 14 (1): 1-7
[21]  Wang Lijun (王丽军). Studies on the kinetics of p-xylene oxidation and the reactor simulation [D]. Hangzhou: Zhejiang University, 2001
[22]  Liu Ruilan (刘瑞兰), Mou Shengjing (牟盛静), Su Hongye (苏宏业), et al. Modeling soft sensor based on support vector machine and particle swarm optimization algorithms [J]. Control Theory and Applications (控制理论与应用), 2006, 23 (6): 895-900

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