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Solving IIR system identification by a variant of particle swarm optimization

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Abstract

A variant of particle swarm optimization (PSO) is represented to solve the infinitive impulse response (IIR) system identification problem. Called improved PSO (IPSO), it makes significant enhancement over PSO. To begin with, the population initialization step makes use of golden ratio to segment solution space so as to obtain high-quality solutions. It is followed by all particles using different inertia weights in velocity updating step, which is beneficial for preserving the balance between global search and local search. Subsequently, IPSO uses normal distribution to disturb the global best particle, which enhances its capacity of escaping from the local optimums. The above three operations cannot only guarantee high-quality solutions, strong global search capacity, and fast convergence rate, but also avoid low diversity, excessive local search, and premature stagnation. These properties of IPSO make it much better suited for IIR system identification problems. IPSO is applied on 12 examples. The experimental results amply demonstrate the capability of IPSO toward obtaining the best objective function values in all the cases. Compared with the other four PSO approaches, IPSO has stronger convergence and higher stability which clearly points out its desirable performance in search accuracy and identifying efficiency.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61403174, 61503165), Jiangsu Province Science Foundation for Youths (No. BK20150239).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China

    De-Xuan Zou

  2. IT & Educational Consultant, Ranchi, Jharkhand, India

    Suash Deb

  3. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, China

    Gai-Ge Wang

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada

    Gai-Ge Wang

  5. Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun, 130117, China

    Gai-Ge Wang

  6. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

    Gai-Ge Wang

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  1. De-Xuan Zou
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Correspondence to Gai-Ge Wang.

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Zou, DX., Deb, S. & Wang, GG. Solving IIR system identification by a variant of particle swarm optimization. Neural Comput & Applic 30, 685–698 (2018). https://doi.org/10.1007/s00521-016-2338-0

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