Neural network implementation of fuzzy inference for approximate case-based reasoning

Liya Ding

doi:10.1117/12.2283786

28 June 1994 Neural network implementation of fuzzy inference for approximate case-based reasoning

Liya Ding

Proceedings Volume 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing; 1031203 (1994) https://doi.org/10.1117/12.2283786
Event: SPIE Institutes for Advanced Optical Technologies 12, 1994, Bellingham, WA, United States

Abstract

In this chapter, we point out the symbolical-numerical duality of fuzzy logic and the rule-case duality of fuzzy rules in approximate reasoning. By the former, we can use a node of a neural network to represent a fuzzy proposition for the symbolic and the value passing the node (input or output) for the numeric. By the latter, we can construct a neural network structure for describing the relations of fuzzy rules and modifythe weights of the neural network to realize learning from cases (examples). The concept of the so-called approximate case-based reasoning' and its neural network implementation is set up on the above understanding. We first give the basic mechanism of approximate case-based reasoning and the neural network implementation, then extend it to more general and complex cases by several examples.

Citation Download Citation

Liya Ding "Neural network implementation of fuzzy inference for approximate case-based reasoning", Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031203 (28 June 1994); https://doi.org/10.1117/12.2283786

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