Learning and adaptation in fuzzy neural systems

Madan M. Gupta

doi:10.1117/12.58590

1 March 1992 Learning and adaptation in fuzzy neural systems

Madan M. Gupta

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58590
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Madan M. Gupta "Learning and adaptation in fuzzy neural systems", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58590

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