NDLI: Effect of Finite Register Length on Bacterial Foraging Optimization based ICA and Constrained Genetic Algorithm based ICA Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Acharya, D.P. Panda, G. Lakshmi, Y.V.S.
Copyright Year	2008
Description	Author affiliation: NIT, Rourkela (Acharya, D.P.)
Abstract	Independent Component Analysis (ICA) technique separates mixed signals blindly without any information of the mixing system. bacterial foraging optimization based ICA (BFOICA) and constrained genetic algorithm based ICA (CGAICA) are two recently developed derivative free evolutionary computational ICA techniques. In BFOICA the foraging behavior of E.coli bacteria present in our intestine is mimicked for evaluation of independent components (IC) where as in CGAICA Genetic Algorithm is used for IC estimation in a constrained manner. The present work evaluates the error performance of BFOICA and CGAICA algorithm for its fixed-point implementation. Simulation study is carried on both fixed and floating point ICA algorithms. It is observed that the word length greatly influences the separation performance. A comparison of fixed-point error performance of both the algorithms is also carried out in this work.
Starting Page	244
Ending Page	249
File Size	3165095
Page Count	6
File Format	PDF
ISBN	9781424419234
DOI	10.1109/ICSCN.2008.4447197
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Constraint optimization Algorithm design and analysis Microorganisms Signal processing algorithms Independent component analysis Registers Random variables Signal analysis Genetic algorithms Fixed-point arithmetic
Content Type	Text
Resource Type	Article

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Effect of Finite Register Length on Bacterial Foraging Optimization based ICA and Constrained Genetic Algorithm based ICA Algorithm

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