| Description |
1 online resource (xv, 100 pages) : illustrations |
| Series |
Studies in fuzziness and soft computing ; 234 |
|
Studies in fuzziness and soft computing ; 234.
|
| Contents |
Introduction -- The FARB -- The FARB-ANN Equivalence -- Rule Simplification -- Knowledge Extraction Using the FARB -- Knowledge-Based Design of ANNs -- Conclusions and Future Research -- A Proofs -- B Details of the LED Recognition Network |
| Summary |
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 |
| Bibliography |
Includes bibliographical references (pages 89-98) and index |
| Notes |
Print version record |
| Subject |
Neural networks (Computer science)
|
|
Fuzzy logic.
|
|
Neural Networks, Computer
|
|
Neural networks (Computer science)
|
|
Fuzzy logic.
|
|
Ingénierie.
|
|
Fuzzy logic
|
|
Neural networks (Computer science)
|
| Form |
Electronic book
|
| Author |
Margaliot, Michael
|
| ISBN |
9783540880776 |
|
3540880771 |
|
9783540880769 |
|
3540880763 |
|
