In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB.
The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.
Author(s): Eyal Kolman, Michael Margaliot (auth.)
Series: Studies in Fuzziness and Soft Computing 234
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2009
Language: English
Pages: 100
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
Front Matter....Pages -
Introduction....Pages 1-12
The FARB....Pages 13-19
The FARB–ANN Equivalence....Pages 21-35
Rule Simplification....Pages 37-40
Knowledge Extraction Using the FARB....Pages 41-57
Knowledge-Based Design of ANNs....Pages 59-76
Conclusions and Future Research....Pages 77-81
Back Matter....Pages -
