Title: A new design method using opposition-based BAT algorithm for IIR system identification problem

Authors: Suman Kumar Saha; Rajib Kar; Durbadal Mandal; Sakti Prasad Ghoshal; Vivekananda Mukherjee

Addresses: Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Department of Electrical Engineering, Indian School of Mines, Dhanbad, 826004, Jharkhand, India

Abstract: BAT algorithm (BA) is a meta-heuristic algorithm, based on the echolocation behaviour of bats. In this paper, optimal set of filter coefficients is searched by the modified optimisation methodology called opposition-based BAT algorithm (OBA) for infinite impulse response (IIR) system identification problem. Opposition based numbering concept is embedded into the primary foundation of BA metaphorically to enhance the convergence speed and performance for finding better near-global optimal solution. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters of OBA technique. When tested against standard benchmark examples, for same and reduced order models, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm (RGA), differential evolution (DE) and particle swarm optimisation (PSO) in terms of accuracy and convergence speed.

Keywords: IIR adaptive filters; evolutionary optimisation; mean square error; MSE; coefficient convergence; bat algorithm; metaheuristics; filter coefficients; infinite impulse response; IIR system identification; simulation; accuracy; convergence speed; bio-inspired computation.

DOI: 10.1504/IJBIC.2013.053508

International Journal of Bio-Inspired Computation, 2013 Vol.5 No.2, pp.99 - 132

Received: 16 Oct 2012
Accepted: 04 Mar 2013
Published online: 31 Mar 2014 *

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