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Neural networks and adaptive expert systems in the CSA approach
Author(s) -
Eberbach Eugeniusz
Publication year - 1993
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550080407
Subject(s) - computer science , expert system , flexibility (engineering) , artificial neural network , artificial intelligence , generalization , inference , computation , machine learning , theoretical computer science , algorithm , mathematics , mathematical analysis , statistics
According to many authors, neural networks and adaptive expert systems may provide the foundations of sixth‐generation computers. Neural networks use lower hardware‐like concepts and they are based on continuous and numeric type computation. On the other hand, adaptive expert systems use inference rules and perform high‐level symbolic computations. the approaches may seem to be totally different, but they do exhibit similar properties: learning, flexibility, parallel search, generalization, and association. This article takes up the problem of the design of a common model for neural networks and adaptive expert systems. For this purpose the Calculus of Self‐Modifiable Algorithms, a general tool for problem solving, is used. This joint approach to expert systems and neural networks emphasize their analogies, rather than their differences. © 1993 John Wiley & Sons, Inc.