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FuNN/2—A fuzzy neural network architecture for adaptive learning and knowledge acquisition by Nikola K. Kasabov; Jaesoo Kim; Michael J. Watts; Andrew R. Gray is a Computer Science article available to read on EtoBox.

What is FuNN/2—A fuzzy neural network architecture for adaptive learning and knowledge acquisition about?

Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalization capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. This paper investigates adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning for a particular model of a fuzzy neural network called FuNN. As well as providing for representing a fuzzy system with an adaptable neural architecture, FuNN also incorporates a genetic algorithm in one of its adaptation modes. A version of FuNN--FuNN/2, which employs triangular membership functions and correspondingly modified learning and adaptation algorithms, is also presented in the paper.

Who reads FuNN/2—A fuzzy neural network architecture for adaptive learning and knowledge acquisition?

Author

Nikola K. Kasabov; Jaesoo Kim; Michael J. Watts; Andrew R. Gray

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0255)

Published

1997

Language

EN

Field

Computer Science (Physical Sciences)