Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

被引：0
作者：
Shuen Wang
Zhenzhen Han
Fucai Liu
Yinggan Tang
机构：
[1] Yanshan University,Institute of Electrical Engineering
[2] National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,undefined
来源：
International Journal of Machine Learning and Cybernetics | 2015年 / 6卷
关键词：
Nonlinear system; Identification; Least squares support vector machine; Adaptive particle swarm optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input–output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.
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页码：981 / 992
页数：11
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