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Proceedings Paper

Learning and adaptation in fuzzy neural systems
Author(s): Madan M. Gupta
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Paper Abstract

In recent years, an increasing number of researchers have become involved in the subject of fuzzy neural networks in the hope of combining the reasoning strength of fuzzy logic and the learning and adaptation power of neural networks. This provides a more powerful tool for fuzzy information processing and for exploring the functioning of human brains. In this paper, an attempt has been made to establish some basic models for fuzzy neurons. First, several possible fuzzy neuron models are proposed. Second, synaptic and somatic learning and adaptation mechanisms are proposed. Finally, the possibility of applying nonfuzzy neural networks approaches to fuzzy systems is also described.

Paper Details

Date Published: 1 March 1992
PDF: 6 pages
Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); doi: 10.1117/12.58590
Show Author Affiliations
Madan M. Gupta, Univ. of Saskatchewan (Canada)


Published in SPIE Proceedings Vol. 1708:
Applications of Artificial Intelligence X: Machine Vision and Robotics
Kevin W. Bowyer, Editor(s)

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