Design of Interpretable Fuzzy Systems

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Design of Interpretable Fuzzy Systems by Krzysztof Cpałka, Springer International Publishing
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Author: Krzysztof Cpałka ISBN: 9783319528816
Publisher: Springer International Publishing Publication: January 31, 2017
Imprint: Springer Language: English
Author: Krzysztof Cpałka
ISBN: 9783319528816
Publisher: Springer International Publishing
Publication: January 31, 2017
Imprint: Springer
Language: English

This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.

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