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02526nam a22005055i 4500 |
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978-3-540-88077-6 |
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DE-He213 |
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20151204163303.0 |
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cr nn 008mamaa |
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100301s2009 gw | s |||| 0|eng d |
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|a 9783540880776
|9 978-3-540-88077-6
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|a 10.1007/978-3-540-88077-6
|2 doi
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|d GrThAP
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|a Q334-342
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|a TJ210.2-211.495
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|a Kolman, Eyal.
|e author.
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|a Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
|h [electronic resource] /
|c by Eyal Kolman, Michael Margaliot.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2009.
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|a XVI, 100 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 234
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|a The FARB -- The FARB–ANN Equivalence -- Rule Simplification -- Knowledge Extraction Using the FARB -- Knowledge-Based Design of ANNs -- Conclusions and Future Research.
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|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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|a Computer science.
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|a Artificial intelligence.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Computer Science.
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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|a Appl.Mathematics/Computational Methods of Engineering.
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| 700 |
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|a Margaliot, Michael.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540880769
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| 830 |
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 234
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-540-88077-6
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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