Comparison between PSO and OLS for NARX Parameter Estimation of a DC Motor

被引：0
|
作者：
Mohamad, M. S. A. [1 ]
Yassin, I. M. [1 ]
Zabidi, A. [1 ]
Taib, M. N. [1 ]
Adnan, R. [1 ]
机构：
[1] Univ Teknol Mara, Fac Elect Engn, Shah Alam, Malaysia
来源：
2013 IEEE SYMPOSIUM ON INDUSTRIAL ELECTRONICS & APPLICATIONS (ISIEA 2013) | 2013年
关键词：
Nonlinear System Identification; NARX; DC Motor;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Recent works suggest that the Particle Swarm Optimization (PSO) algorithm is a highly-efficient optimization technique for structure selection of NARMAX and its derivative models. This research extends those findings by proposing PSO for parameter estimation of a Nonlinear Auto-Regressive with Exogenous (NARX) model for a Direct Current (DC) motor. The proposed method was compared to the established Orthogonal Least Squares (OLS) method. The findings indicate that PSO was comparable to OLS in solving the Least Squares (LS) parameter estimation problem posed in the NARX model.
引用
收藏
页码：27 / 32
页数：6
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