[1]王杰,陈锴鹏..基于决策函数及PSO优化的SVM预测控制应用研究[J].郑州大学学报(工学版),2013,34(02):53-56.[doi:10.3969/j.issn.1671-6833.2013.02.014]
　WANG Jie,CHEN Kai-peng.Application Study of SVM Predictive Control Based on DecisionFunctions Simplification and Pso Optimization[J].Journal of Zhengzhou University (Engineering Science),2013,34(02):53-56.[doi:10.3969/j.issn.1671-6833.2013.02.014]
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基于决策函数及PSO优化的SVM预测控制应用研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
34
期数:
2013年02期
页码:
53-56
栏目:
出版日期:
2013-03-28

文章信息/Info

Title:
Application Study of SVM Predictive Control Based on DecisionFunctions Simplification and Pso Optimization
作者:
王杰; 陈锴鹏.
郑州大学电气工程学院,河南郑州,450001, 郑州大学电气工程学院,河南郑州,450001
Author(s):
WANG Jie; CHEN Kai-peng
School of Electrical Engineering，Zhengzhou University，Zhengzhou 450001，China
关键词:
支持向量机;  决策函数;  粒子群算法;  预测控制
Keywords:
SVM;  decision function; PSO;  predictive control
分类号:
TP181
DOI:
10.3969/j.issn.1671-6833.2013.02.014
文献标志码:
A
摘要:
SVM处理大样本问题时性能明显不如神经网络,因此笔者利用矩阵变换进行决策函数的简化来提升SVM的训练速度,对SVM建模时非必需的支持向量进行约简,并引入一个松弛变量来提升约简效果.实验证明,约简后支持向量个数减少三分之一以上.SVM所建立的模型进行线性化之后应用于预测控制当中,采用PSO算法来选择最优的SVM参数和计算预测控制的最优控制律.通过对水泥回转窑窑尾烟室温度的数据进行实验仿真,结果表明该方法可以提高系统响应速度,减小系统响应的超调量.
Abstract:
For large-scale samples,SVM does not perform as well as neural networks and this paper tries toimprove the training speed by simplify the decision functions by matrix transform. We simplify the unnecessarysupport vectors in SVM modeling and introduce a relaxation factor in order to improve the effects of simplifica-tion.Experiment shows that the number of support vectors is redused by at least one third. Using the modelnonlinear model built by SVM after linearizing as the predictive model of predictive control. PSO was used toselect the best SVM parameters and computing the optimal control law of predictive control. The method canaccelerate the response and shorten the overshoot through a simulation of a cement rotary kiln.

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更新日期/Last Update: 1900-01-01