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Hybridisation of GA and PSO to Optimise N-tuples

Hannan Bin Azhar, M.A. and Deravi, Farzin and Dimond, Keith (2009) Hybridisation of GA and PSO to Optimise N-tuples. In: 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, pp. 1815-1820. ISBN 978-1-4244-2793-2. (doi:10.1109/ICSMC.2009.5346854) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

Abstract

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This paper is concerned with the design of a Weightless Neural Network, which demoposes a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. This paper investigates the hybridisation of Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) techniques in search of better connection maps to the N-tuples. Experiments were conducted to evaluate the proposed metho9d of applying the trained classifier to recognise hand-printed digits from a widely used database compiled by US National Institute of Standards and Technology (NIST).

Item Type: Book section
DOI/Identification number: 10.1109/ICSMC.2009.5346854
Uncontrolled keywords: N-tuples, GA, PSO, Pattern Recognition, WNN
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.P3 Pattern Recognition
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 04 Jan 2010 12:07 UTC
Last Modified: 01 Aug 2019 10:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/23510 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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