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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach [electronic resource] /by Eyal Kolman, Michael Margaliot.

by Kolman, Eyal [author.]; Margaliot, Michael [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Fuzziness and Soft Computing: 234Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.Description: online resource.ISBN: 9783540880776.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online
Contents:
The FARB -- The FARB–ANN Equivalence -- Rule Simplification -- Knowledge Extraction Using the FARB -- Knowledge-Based Design of ANNs -- Conclusions and Future Research.
In: Springer eBooksSummary: 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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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.

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