NARMAX Identification of DC Motor Model Using Repulsive Particle Swarm Optimization

被引：0
|
作者：
Supeni, E.
Yassin, Ihsan M.
Ahmad, A.
Rahman, F. Y. Abdul
机构：
来源：
CSPA: 2009 5TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, PROCEEDINGS | 2009年
关键词：
Neural Network Applications; System Identification; DC Motors; Stochastic Approximation;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper explores the usage of repulsive particle swarm optimization (RPSO) to perform Non-linear Auto-Regressive with exogenous input (NARMAX) system identification of Direct Current (DC) motor. The NARMAX model was constructed using a recurrent Artificial Neural Network (ANN) model by Rahim and Taib and Yassin et al. The comparison result was made between RPSO method and inertia weight-based PSO method by Yassin et al. to train the NARMAX model The result shows that RPSO yielded comparable performance to the inertia weight-based PSO method in determining NARMAX coefficients in the model.
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页码：1 / 7
页数：7
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