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IIR system identification using cat swarm optimization |
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| Authors: | Ganapati Panda Pyari Mohan Pradhan Babita Majhi |
| Affiliation: | 1. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, India;2. Department of Information Technology, ITER, SOA University Bhubaneswar, India;1. College of Information Science & Engineering, Northeastern University, Shenyang 110004, China;2. Graduate School of Business and Law, RMIT University, Melbourne 3000, Australia;3. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China;1. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taiwan;2. Ecological and Hazard Mitigation Engineering Researching Center, National Taiwan University of Science and Technology, Taiwan;1. Institute of Future Energy, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia;2. Department of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia |
| Abstract: | Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification. |
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