Particle Swarm Optimization for NARX structure selection — Application on DC motor model

Author

Yassin, Ihsan Mohd ; Taib, Mohd Nasir ; Rahim, Norasmadi Abdul ; Salleh, Mohd Khairul Mohd ; Abidin, Husna Zainol

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia

fYear

2010

fDate

3-5 Oct. 2010

Firstpage

456

Lastpage

462

Abstract

This paper explores the application of the Binary Particle Swarm Optimization (BPSO) by (Kennedy and Eberhart, 1997) to perform model structure selection of a Nonlinear Auto-Regressive model with Exogenous Inputs (NARX) identification of a Direct Current (DC) motor. We describe the application of BPSO for model structure selection, by representing its particles´ solutions as probabilities of change (bit flip) of a binary string. The binary string was then used to select a set of regressors from the regressor matrix, then estimate the coefficients (linear least squares solution) of the reduced regressor matrix using QR decomposition. Tests performed on a simulated DC motor dataset showed that the BPSO-based selection method has the potential to become an effective method to determine parsimonious NARX model structure in the system identification model.

Keywords

DC motors; autoregressive processes; machine theory; matrix algebra; particle swarm optimisation; probability; BPSO-based selection method; DC motor model; NARX structure selection; QR decomposition; binary particle swarm optimization; binary string; direct current motor; exogenous input identification; nonlinear autoregressive model; probability of change; regressor matrix; system identification model; Autoregressive processes; DC motors; Mathematical model; Optimization; System identification; Testing; Training; DC motor; Non-linear AutoRegressive Model with Exogenous Inputs (NARX); System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on

Conference_Location

Penang

Print_ISBN

978-1-4244-7645-9

Type

conf

DOI

10.1109/ISIEA.2010.5679421

Filename

5679421