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Proceedings Paper

Foundations of fuzzy neural networks
Author(s): Madan M. Gupta; Dandina Hulikunta Rao
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Paper Abstract

Over the last decade or so, significant advances have been made in two distinct areas: fuzzy logic and computational neural networks. The theory of fuzzy logic provides mathematical strength to compare the uncertainties associated with human cognitive processes, such as thinking and reasoning. Also, it provides a mathematical morphology to emulate certain perceptual and linguistic attributes associated with human cognition. On the other hand, the computational neural network paradigm has evolved in the process of understanding the incredible learning and adaptability of biological neural mechanisms. Neural networks replicate, on a small scale, some of the computational operations observed in biological learning and adaptation. The integration of these two fields, fuzzy logic and neural networks, has given birth to an emerging paradigm--the fuzzy neural networks. The fuzzy neural networks have the potential to capture the benefits of the two fascinating fields, fuzzy logic and neural networks, into a single capsule. The intent of this paper is to provide an introductory look at this emerging research field of fuzzy neural networks.

Paper Details

Date Published: 22 December 1993
PDF: 16 pages
Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); doi: 10.1117/12.165046
Show Author Affiliations
Madan M. Gupta, Univ. of Saskatchewan (Canada)
Dandina Hulikunta Rao, Univ. of Saskatchewan (Canada)

Published in SPIE Proceedings Vol. 2061:
Applications of Fuzzy Logic Technology
Bruno Bosacchi; James C. Bezdek, Editor(s)

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