Authors:
- Presents the state of the art in knowledge-based neurocomputing
- Presents a new connection between artificial neural networks (ANNs) and a special fuzzy rule-base - the all permutations fuzzy rule-base (FARB)
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 234)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Tel Aviv University, Tel Aviv, Israel
Eyal Kolman, Michael Margaliot
Bibliographic Information
Book Title: Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
Authors: Eyal Kolman, Michael Margaliot
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-540-88077-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-88076-9Published: 17 January 2009
Softcover ISBN: 978-3-642-09985-4Published: 21 October 2010
eBook ISBN: 978-3-540-88077-6Published: 18 October 2008
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XVI, 100
Topics: Artificial Intelligence, Mathematical and Computational Engineering