Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification

J. So; William Kenneth Jenkins

doi:10.1109/ACSSC.2013.6810419

Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification

J. So, William Kenneth Jenkins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Infinite impulse response (IIR) adaptive filters have been developed to identify IIIR systems, but system identification is challenging due to non-unimodality of the error surface and the non-linear relationship between the error signal and the system parameters. Cat Swarm Optimization (CSO) was recently introduced to solve optimization problems with a new learning rule to achieve better performance than particle swarm optimization (PSO). Also, it has been used for IIR system identification. This paper examines the parameters of CSO to optimize them for IIR system identification with a few benchmarked IIR plants. Results demonstrate better performance for the CSO algorithm when compared to the inertia-weighted PSO algorithm.

Original language	English (US)
Title of host publication	Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	903-910
Number of pages	8
ISBN (Print)	9781479923908
DOIs	https://doi.org/10.1109/ACSSC.2013.6810419
State	Published - Jan 1 2013
Event	2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 3 2013 → Nov 6 2013

Other

Other	2013 47th Asilomar Conference on Signals, Systems and Computers
Country	United States
City	Pacific Grove, CA
Period	11/3/13 → 11/6/13

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2013.6810419

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Cite this

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title = "Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification",

abstract = "Infinite impulse response (IIR) adaptive filters have been developed to identify IIIR systems, but system identification is challenging due to non-unimodality of the error surface and the non-linear relationship between the error signal and the system parameters. Cat Swarm Optimization (CSO) was recently introduced to solve optimization problems with a new learning rule to achieve better performance than particle swarm optimization (PSO). Also, it has been used for IIR system identification. This paper examines the parameters of CSO to optimize them for IIR system identification with a few benchmarked IIR plants. Results demonstrate better performance for the CSO algorithm when compared to the inertia-weighted PSO algorithm.",

author = "J. So and Jenkins, {William Kenneth}",

year = "2013",

month = jan,

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doi = "10.1109/ACSSC.2013.6810419",

language = "English (US)",

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booktitle = "Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

address = "United States",

note = "2013 47th Asilomar Conference on Signals, Systems and Computers ; Conference date: 03-11-2013 Through 06-11-2013",

}

So, J & Jenkins, WK 2013, Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification. in Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers., 6810419, IEEE Computer Society, pp. 903-910, 2013 47th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 11/3/13. https://doi.org/10.1109/ACSSC.2013.6810419

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AB - Infinite impulse response (IIR) adaptive filters have been developed to identify IIIR systems, but system identification is challenging due to non-unimodality of the error surface and the non-linear relationship between the error signal and the system parameters. Cat Swarm Optimization (CSO) was recently introduced to solve optimization problems with a new learning rule to achieve better performance than particle swarm optimization (PSO). Also, it has been used for IIR system identification. This paper examines the parameters of CSO to optimize them for IIR system identification with a few benchmarked IIR plants. Results demonstrate better performance for the CSO algorithm when compared to the inertia-weighted PSO algorithm.

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