Neural network based approach to knowledge acquisition and expert systems

Nicholas DeClaris, Mu Chun Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Often a major difficulty in the design of expert systems is the process of acquiring the requisite knowledge in the form of production rules. This paper presents a novel class of neural networks which are trained in such a way that they provide an appealing solution to the problem of knowledge acquisition. The value of the network parameters, after sufficient training, are then utilized to generate production rules on the basis of preselected meaningful coordinates. Further the paper provides a mathematical framework for achieving reasonable generalization properties via an appropriate training algorithm (supervised decision-directed learning) with a structure that provides acceptable knowledge representations of the data. The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Editors Anon
PublisherPubl by IEEE
Pages645-650
Number of pages6
ISBN (Print)0780309111
StatePublished - 1993
EventProceedings of 1993 International Conference on Systems, Man and Cybernetics - Le Touquet, Fr
Duration: 17 Oct 199320 Oct 1993

Publication series

NameProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
ISSN (Print)0884-3627

Conference

ConferenceProceedings of 1993 International Conference on Systems, Man and Cybernetics
CityLe Touquet, Fr
Period17/10/9320/10/93

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