Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
Springer Berlin (Verlag)
978-3-642-09985-4 (ISBN)
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.
The FARB.- The FARB-ANN Equivalence.- Rule Simplification.- Knowledge Extraction Using the FARB.- Knowledge-Based Design of ANNs.- Conclusions and Future Research.
Erscheint lt. Verlag | 21.10.2010 |
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Reihe/Serie | Studies in Fuzziness and Soft Computing |
Zusatzinfo | XVI, 100 p. |
Verlagsort | Berlin |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 185 g |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
Technik | |
Schlagworte | All-Permutations Fuzzy Rule Base • Cognition • Equivalence • fuzzy • Fuzzy Logic • Fuzzy Rule-Based Systems • Knowedge Refinement • Knowledge-Based Neurocomputing • knowledge extraction • Knowledge Insertion |
ISBN-10 | 3-642-09985-8 / 3642099858 |
ISBN-13 | 978-3-642-09985-4 / 9783642099854 |
Zustand | Neuware |
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