Complexity Management in Fuzzy Systems [electronic resource] :A Rule Base Compression Approach / by Alexander Gegov.
by Gegov, Alexander [author.]; SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 211Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.Description: XV, 351 p. online resource.ISBN: 9783540388852.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 | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TA640-643 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | TA329-348 (Browse shelf) | Available |
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| QA75.5-76.95 Artificial Neural Networks – ICANN 2006 | QA75.5-76.95 Artificial Neural Networks – ICANN 2006 | TA329-348 Complexity Management in Fuzzy Systems | TA640-643 Complexity Management in Fuzzy Systems | QA75.5-76.95 Business Process Management | HD28-70 Strategic Closed-Loop Supply Chain Management | RM1-950 Encyclopedia of Molecular Pharmacology |
Basic Types of Fuzzy Rule Based Systems -- Rule Base Reduction Methods for Fuzzy Systems -- Formal Presentation of Fuzzy Rule Based Systems -- Formal Manipulation of Fuzzy Rule Based Systems -- Formal Manipulation with Special Rule Bases -- Formal Transformation of Fuzzy Rule Based Systems -- Formal Transformation of Feedback Rule Bases -- Formal Simplification of Fuzzy Rule Based Systems -- Conclusion.
This book presents a systematic study on the inherent complexity in fuzzy systems, resulting from the large number and the poor transparency of the fuzzy rules. The study uses a novel approach for complexity management, aimed at compressing the fuzzy rule base by removing the redundancy while preserving the solution. The compression is based on formal methods for presentation, manipulation, transformation and simplification of fuzzy rule bases, which are illustrated by algorithms as well as results from numerous examples and two case studies. The results are directly applicable or easily extendable to a wide class of fuzzy systems and detailed benchmarks for expanding these systems to new areas such as fuzzy networks and fuzzy multi-agent systems are introduced. The intended readers are people from both academia and industry, who would be interested in building and implementing advanced fuzzy systems.
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