|本期目录/Table of Contents|

[1]李军,赵峰.最小二乘小波支持向量机在非线性控制中的应用[J].电机与控制学报,2009,(04):620-625.
 LI Jun,ZHAO Feng.Application to nonlinear control using least squares wavelet support vector machines[J].,2009,(04):620-625.
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最小二乘小波支持向量机在非线性控制中的应用(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2009年04
页码:
620-625
栏目:
出版日期:
2009-07-15

文章信息/Info

Title:
Application to nonlinear control using least squares wavelet support vector machines
作者:
李军; 赵峰;
兰州交通大学自动化与电气工程学院;
Author(s):
LI Jun; ZHAO Feng
School of Automation & Electrical Engineering; Lanzhou Jiaotong University; Lanzhou 730070; China
关键词:
支持向量机 最小二乘支持向量机 小波核 Cholesky算法 非线性动态系统 自适应控制
Keywords:
support vector machines least square support vector machines wavelet kernel Cholesky algorithm nonlinear dynamical systems adaptive control
分类号:
TP181
DOI:
-
文献标志码:
A
摘要:
结合小波技术和支持向量机,提出了一种基于多维允许小波核的最小二乘小波支持向量机,其小波核函数具有近似正交和适用于信号局部分析的特点。同时,给出了一种有效求解最小二乘小波支持向量机的Cholesky分解算法。将最小二乘小波支持向量机应用在非线性系统的自适应控制上,仿真结果表明,与最小二乘支持向量机、多层前向神经网络或模糊逻辑系统相比,最小二乘小波支持向量机均能给出较好的性能,显示出快速而稳定的学习速度,而且在相同条件下,最小二乘小波支持向量机比最小二乘支持向量机的逼近精确度提高了一个数量级。所提出的用于非线性动态系统自适应控制的最小二乘小波支持向量机方法具有效性和实用性。
Abstract:
A form of least squares wavelet support vector machines(LS-WSVM) using multi-dimensional admissible wavelet kernel was proposed,which combined the wavelet techniques with support vector machines(SVM).The wavelet kernel was characterized by its local analysis and approximate orthogonality.Simultaneously,an efficient implementation algorithm via Cholesky factorisation for LS-WSVM was also given.The LS-WSVM was then applied to adaptive control of nonlinear dynamical systems.Simulation results reveal that the m...

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备注/Memo

备注/Memo:
甘肃省自然科学基金(0803RJZA023)
更新日期/Last Update: 2010-05-25