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130626 ||| eng |
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|a 9783540388852
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|a Gegov, Alexander
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| 245 |
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|a Complexity Management in Fuzzy Systems
|h Elektronische Ressource
|b A Rule Base Compression Approach
|c by Alexander Gegov
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| 250 |
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|a 1st ed. 2007
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2007, 2007
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| 300 |
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|a XV, 349 p
|b online resource
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| 505 |
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|a 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
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| 653 |
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|a Engineering mathematics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Control theory
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| 653 |
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|a Systems Theory, Control
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| 653 |
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|a System theory
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Engineering / Data processing
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| 653 |
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|a Mathematical and Computational Engineering Applications
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Studies in Fuzziness and Soft Computing
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| 028 |
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|a 10.1007/978-3-540-38885-2
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| 856 |
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|u https://doi.org/10.1007/978-3-540-38885-2?nosfx=y
|x Verlag
|3 Volltext
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|a 003
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|a 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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