NARMAX identification of DC motor model using repulsive particle swarm optimization

Author

Supeni, E. ; Yassin, Ihsan M. ; Ahmad, A. ; Rahman, F. Y Abdul

Author_Institution

Fac. of Electr. Eng., UiTM Shah Alam, Shah Alam

fYear

2009

fDate

6-8 March 2009

Firstpage

1

Lastpage

7

Abstract

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.

Keywords

DC motors; autoregressive moving average processes; learning (artificial intelligence); nonlinear systems; particle swarm optimisation; power engineering computing; recurrent neural nets; ANN model; DC motor model; NARMAX model training; exogenous input system identification; inertia weight-based PSO method; nonlinear auto-regressive moving average process; recurrent artificial neural network; repulsive particle swarm optimization; Artificial neural networks; Clustering algorithms; DC motors; Equations; Neural networks; Particle swarm optimization; Power system modeling; Signal processing; Stochastic systems; System identification; DC Motors; Neural Network Applications; Stochastic Approximation; System Identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing & Its Applications, 2009. CSPA 2009. 5th International Colloquium on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-4151-8

Electronic_ISBN

978-1-4244-4152-5

Type

conf

DOI

10.1109/CSPA.2009.5069176

Filename

5069176