How to Interpret Neural Networks in Terms of Fuzzy Logic?

Authors

Sompong Dhompongsa
Vladik Kreinovich, The University of Texas at El PasoFollow
Hung T. Nguyen

Publication Date

10-2001

Comments

UTEP-CS-01-26.

Published in: Proceedings of the Second Vietnam-Japan Bilateral Symposium on Fuzzy Systems and Applications VJFUZZY'2001, Hanoi, December 7-8, 2001, pp. 184-190.

Abstract

Neural networks are a very efficient learning tool, e.g., for transforming an experience of an expert human controller into the design of an automatic controller. It is desirable to reformulate the neural network expression for the input-output function in terms most understandable to an expert controller, i.e., by using words from natural language. There are several methodologies for transforming such natural-language knowledge into a precise form; since these methodologies have to take into consideration the uncertainty (fuzziness) of natural language, they are usually called fuzzy logics.