Share Email Print

Proceedings Paper

Neural network implementation of fuzzy inference for approximate case-based reasoning
Author(s): Liya Ding
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 28 June 1994
PDF: 29 pages
Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031203 (28 June 1994); doi: 10.1117/12.2283786
Show Author Affiliations
Liya Ding, National Univ. of Singapore (Singapore)

Published in SPIE Proceedings Vol. 10312:
Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing
Sunanda D. Mitra; Madan M. Gupta; Wolfgang F. Kraske, Editor(s)

© SPIE. Terms of Use
Back to Top