基于混沌万有引力搜索算法的SVM参数优化及应用

doi:10.11896/j.issn.1002-137X.2015.04.049

计算机科学 ›› 2015, Vol. 42 ›› Issue (4): 240-243.doi: 10.11896/j.issn.1002-137X.2015.04.049

基于混沌万有引力搜索算法的SVM参数优化及应用

龚安,吕倩,胡长军,康忠健,李华昱

北京科技大学计算机与通信工程学院北京100083;中国石油大学华东青岛266580,中国石油大学华东青岛266580,北京科技大学计算机与通信工程学院北京100083,中国石油大学华东青岛266580,中国石油大学华东青岛266580

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金:基于阻抗模型故障特征匹配法的含DG配电网故障测距研究(61271001),中央高校基本科研业务费专项资金(14CX02030A)资助

Parameter Optimization and Application of SVM Based on Chaos Gravitational Search Algorithm

GONG An, LV Qian, HU Chang-jun, KANG Zhong-jian and LI Hua-yu

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 针对万有引力搜索算法存在局部优化能力差的问题,引入混沌序列和遗传算法的交叉思想对其改善,并将其应用于SVM的参数优化,通过仿真实验验证了该SVM模型具有更高的精度。最后将该模型应用于火电厂一次风机的状态监测,实验结果表明该模型是有效的。

关键词: 万有引力搜索算法,混沌序列,交叉,SVM,状态监测

Abstract: Chaotic series and the crossover of genetic algorithm were introduced into gravitational search algorithm to overcome its poor local optimization problem.The improved algorithm was used for the SVM parameters optimization.And the simulated experiments show that the SVM model has a higher accuracy.At last the model was applied to the condition monitoring of primary air fan in power plant.And the experimental results indicate that the model is valid.

Key words: Gravitational search algorithm,Chaotic series,Crossover,SVM,Condition monitoring

龚安,吕倩,胡长军,康忠健,李华昱. 基于混沌万有引力搜索算法的SVM参数优化及应用[J]. 计算机科学, 2015, 42(4): 240-243. https://doi.org/10.11896/j.issn.1002-137X.2015.04.049

GONG An, LV Qian, HU Chang-jun, KANG Zhong-jian and LI Hua-yu. Parameter Optimization and Application of SVM Based on Chaos Gravitational Search Algorithm[J]. Computer Science, 2015, 42(4): 240-243. https://doi.org/10.11896/j.issn.1002-137X.2015.04.049

参考文献

[1] 刘春波,王鲜芳,潘丰.基于蚁群优化算法的支持向量机参数选择及仿真[J].中南大学学报:自然科学版,2008,39(6):1309-1313
[2] 张艳秋,王蔚.利用遗传算法优化的支持向量机垃圾邮件分类[J].计算机应用,2009,29(10):2755-2757
[3] 邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及其应用[J].控制理论与应用,2006,23(5):740-743,748
[4] Rashedi E,Nezamabadi-pour H,Saryazdi S.GSA:a gravitational Searchal gorithm[J].Information Sciences,2009,179(13):2232-2248
[5] Rashedi E,Nezamabadi-pour H,Saryazdi S.BGSA:Binary gravi-tational search algorithm [J].Natural Computing,2010,9(3):727-745
[6] Liao Gwo-ching,Tsao Ta-peng.Application Embedded Chaos Search Immune Genetic Algorithm for Shortterm Unit Commitment [J].Electric Power Systems Research,2004,71(2):135-144
[7] Alatas B,Akin E,Ozer A B.Chaos embedded particle swarm optimization algorithm[J].Chaos,Solutions and Fractals,2009,40(4):1715-1734
[8] Tang K S,Man K F,Kwong S,et al.Genetic algorithms and their applications[J].IEEE Signal Processing Magazine,1996,13(6):22-37
[9] 李敏强,寇纪淞,等.遗传算法的基本理论与应用[M].北京:科学出版社,2002
[10] 左兴权,李士勇.一类自适应免疫进化算法[J].控制与决策,2004,19(3):252-256
[11] 郭鹏,Infield D,杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报,2011,31(32):129-136

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基于混沌万有引力搜索算法的SVM参数优化及应用

Parameter Optimization and Application of SVM Based on Chaos Gravitational Search Algorithm

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