Knowledge-Based Neurocomputing.. This item is unavailable.
Language: English
Published by Berlin, Springer Berlin / Heidelberg, 2009
- Hardcover
- Used
Seller: Antiquariat Bookfarm, Löbnitz, GermanyAntiquariat Bookfarm
AbeBooks seller since October 28, 2009
Condition: Used - Very good
US$ 47.51
Item description from seller
108 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. 9783540880769 Sprache: Englisch Gewicht in Gramm: 550.
Seller Inventory # 2340506
- Title
- Knowledge-Based Neurocomputing.
- Author
- Eyal, Kolman:
- Publisher
- Berlin, Springer Berlin / Heidelberg
- Publication year
- 2009
- Condition
- Gut
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3540880763
- ISBN 13
- 9783540880769
- Item weight
- 550 grams
- Seller catalogs
- TA Allgemeine Naturwissenschaften
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.