Title: Infinite impulse response system identification using antlion optimisation algorithm

Authors: Sandeep Singh; Alpana Shekhar; Chakshu Kalra; Shubham Kaushik; Tarun Kumar Rawat; Alaknanda Ashok

Addresses: Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, Delhi, India; Department of Electronics and Communication Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand, India ' Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, Delhi, India ' Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, Delhi, India ' Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, Delhi, India ' Department of Electronics and Communication Engineering, Netaji Subhash University of Technology, Delhi, India ' Department of Electronics and Communication Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand, India; Department of Electrical Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Abstract: This paper focuses on infinite impulse response (IIR) system identification which uses a recent nature inspired algorithm called antlion optimisation (ALO). The system identification problem is concerned with determining the viable parameters by minimising the cost function. Generally, gradient-based techniques are mostly used for IIR system identification. However, these traditional algorithms face the problem of getting trapped in local solution. So to get rid of this problem, a novel ALO algorithm is used for IIR system identification. The ALO is inspired by the preying process of antlions on the ants. The algorithm is free of the issues faced by the traditional techniques. The performance of ALO algorithm is measured using two measures mean square error (MSE) which is taken as cost function and the convergence profile. The results obtained using ALO are compared with those of the particle swarm optimisation (PSO) algorithm and cat swarm optimisation (CSO) algorithm. The obtained results confirmed that the algorithm surpasses the performance of the existing algorithms.

Keywords: system identification; mean square error; MSE; convergence curve; antlion algorithm; ALO; particle swarm optimisation; PSO; cat swarm optimisation; CSO.

DOI: 10.1504/IJHPCN.2021.120738

International Journal of High Performance Computing and Networking, 2021 Vol.17 No.1, pp.1 - 7

Received: 03 Jul 2020
Accepted: 05 Jan 2021
Published online: 07 Feb 2022 *

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