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 proceedingConference 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 languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages903-910
Number of pages8
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Cite this

So, J., & Jenkins, W. K. (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 (pp. 903-910). [6810419] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810419