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Design of interpretable fuzzy systems

Cpalka, Krzysztof.

Design of interpretable fuzzy systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design of interpretable fuzzy systemsby Krzysztof Cpalka.
作者:	Cpalka, Krzysztof.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xi, 196 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Fuzzy systems.
電子資源:	http://dx.doi.org/10.1007/978-3-319-52881-6
ISBN:	9783319528816$q(electronic bk.)

Design of interpretable fuzzy systems
Cpalka, Krzysztof.

Design of interpretable fuzzy systems[electronic resource] /by Krzysztof Cpalka. - Cham :Springer International Publishing :2017. - xi, 196 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6841860-949X ;. - Studies in computational intelligence ;v. 216..

Preface -- Acknowledgements -- Chapter1: Introduction -- Chapter2: Selected topics in fuzzy systems designing -- Chapter3: Introduction to fuzzy system interpretability -- Chapter4: Improving fuzzy systems interpretability by appropriate selection of their structure -- Chapter5: Interpretability of fuzzy systems designed in the process of gradient learning -- Chapter6: Interpretability of fuzzy systems designed in the process of evolutionary learning -- Chapter7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control -- Chapter8: Case study: interpretability of fuzzy systems applied to identity veriﬁcation -- Chapter9: Concluding remarks and future perspectives -- Index.

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.

ISBN: 9783319528816$q(electronic bk.)

Standard No.: 10.1007/978-3-319-52881-6doiSubjects--Topical Terms:

182084
Fuzzy systems.

LC Class. No.: QA402

Dewey Class. No.: 511.313

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