Effectiveness of the bio-inspired firefly algorithm in adaptive signal processing for nonlinear systems

M. Hussain, William Kenneth Jenkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

In this paper the performance of the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) algorithm, the Modified Particle Swarm Optimization (MPSO) algorithm, and the Lévy Flight Firefly Algorithm (LFFA) are compared for system identification with various types of nonlinear systems. When performing system identification with Volterra nonlinear adaptive structures, matched-order fixed-nonlinearity LNL filter structures, reduced-order adaptable-nonlinearity LNL filter structures and neural networks the LFFA generally demonstrates faster convergence rates and lower minimum mean square errors (MMSE) compared to the GA, PSO, and MPSO algorithms. This work includes performance comparisons of these algorithms when applied to nonlinear system identification.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019May 29 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
Country/TerritoryJapan
CitySapporo
Period5/26/195/29/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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