28 June 1994 Neural network implementation of fuzzy inference for approximate case-based reasoning
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Liya Ding, 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); doi: 10.1117/12.2283786; https://doi.org/10.1117/12.2283786
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