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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
(Englisch)
Studies in Fuzziness and Soft Computing 234
Eyal Kolman & Michael Margaliot

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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach

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  • Lauinger, Sonia
    Lauinger Verlag
    Heinrich-Köhler-Platz 8
    DE-76187 Karlsruhe
    +49 721 47037027
    mail@lauinger-verlag.de
    Frau Sonia Lauinger
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Produktbeschreibung

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)


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.


The FARB.- The FARB–ANN Equivalence.- Rule Simplification.- Knowledge Extraction Using the FARB.- Knowledge-Based Design of ANNs.- Conclusions and Future Research.

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.


This book details the state-of-the-art in knowledge-based neurocomputing. It introduces a novel fuzzy-rule base known as Fuzzy All-permutations Rule-Base (FARB) and presents new connections between artificial neural networks and FARB.

The FARB.- The FARB-ANN Equivalence.- Rule Simplification.- Knowledge Extraction Using the FARB.- Knowledge-Based Design of ANNs.- Conclusions and Future Research.

Inhaltsverzeichnis



The FARB.- The FARB¿ANN Equivalence.- Rule Simplification.- Knowledge Extraction Using the FARB.- Knowledge-Based Design of ANNs.- Conclusions and Future Research.


Klappentext

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. rnrnThe 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.




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)



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