Evolving learning mechanism for a general computing network model

Q Wu, TM McGinnity, G Prasad, D Bell

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

Abstract

In this paper, an evolving learning mechanism is proposed for general computing network model to make decisions in intelligent systems. The novel mechanism is performed by means of a set of computing cell operations such as self-generation, growth, self-division, and death. Under the mechanism, a computing network grows up to a mature network. A hidden cell in the network is defined as a condition matching-unit in response to a fuzzy sub-superspace in multiple-dimension input superspace. A sense-function is defined to represent connections from a hidden cell to input cells. The range and edge vagueness of the sense-function are determined by evolving learning mechanism when sample instances are presented to the network. This network is able to learn from a very few training instances to make decisions for unseen instances. The benchmark data sets from the UCI machine learning repository are applied to test the network and comparable results are obtained.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1914-1919
Number of pages6
DOIs
Publication statusPublished - Oct 2005
Event2005 IEEE International Conference on Systems, Man, and Cybernetics, Hawaii, USA -
Duration: 1 Oct 2005 → …

Conference

Conference2005 IEEE International Conference on Systems, Man, and Cybernetics, Hawaii, USA
Period1/10/05 → …

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