Particle swarm optimization for NARX structure selection - Application on DC motor model

• Published in: ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications
• Main Author: Yassin I.M.; Taib M.N.; Rahim N.A.; Salleh M.K.M.; Abidin H.Z.
• Format: Conference paper
• Language: English
• Published: 2010
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251548905&doi=10.1109%2fISIEA.2010.5679421&partnerID=40&md5=64769d6a3989b367b2e507e642131ae6
• DOI: 10.1109/ISIEA.2010.5679421

This paper explores the application of the Binary Particle Swarm Optimization (BPSO) by (Kennedy and Eberhart, 1997) to perform model structure selection of a Non-linear 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. ©2010 IEEE.