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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kolman, Eyal (Autor), Margaliot, Michael (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Studies in Fuzziness and Soft Computing, 234
Temas:
Acceso en línea:Texto Completo

MARC

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505 0 |a The FARB -- The FARB-ANN Equivalence -- Rule Simplification -- Knowledge Extraction Using the FARB -- Knowledge-Based Design of ANNs -- Conclusions and Future Research. 
520 |a 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. 
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