Application of adaptive least square support vector machines in nonlinear system identification

被引：0
|
作者：
Wang, Xiaodong [1 ]
Liang, Weifeng [1 ]
Cai, Xiushan [1 ]
Lv, Ganyun [1 ]
Zhang, Changjiang [1 ]
Zhang, Haoran [1 ]
机构：
[1] Zhejiang Normal Univ, Coll Informat Sci & Engn, Jinhua 321004, Zhejiang, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
Nonlinear systems; identification; support vector machines;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Training problem of least squares support vector machine (LS-SVM) is solved by finding a solution to a set of linear equations. This makes online adaptive implementation of the algorithm feasible. In this paper, an adaptive algorithm for the purpose of nonlinear system identification is proposed. Using this training algorithm, a variant of support vector machine has been developed called adaptive LS-SVM. The adaptive LS-SVM is especially useful on online system identification. Several pertinent numerical simulations have shown the validity of the proposed method.
引用
收藏
页码：1897 / +
页数：2
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