Design of interpretable fuzzy systems
- Responsibility
- Krzysztof Cpałka.
- Digital
- text file
- Publication
- Cham, Switzerland : Springer, 2017.
- Physical description
- 1 online resource (xi, 196 pages) : illustrations
- Series
- Studies in computational intelligence ; v. 684. 1860-949X
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Cpałka, Krzysztof, author.
Contents/Summary
- Bibliography
- Includes bibliographical references and index.
- Contents
-
- Preface.- Acknowledgements.- Chapte
- r1: Introduction.- Chapte
- r2: Selected topics in fuzzy systems designing.- Chapte
- r3: Introduction to fuzzy system interpretability.- Chapte
- r4: Improving fuzzy systems interpretability by appropriate selection of their structure.- Chapte
- r5: Interpretability of fuzzy systems designed in the process of gradient learning.- Chapte
- r6: Interpretability of fuzzy systems designed in the process of evolutionary learning.- Chapte
- r7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control.- Chapte
- r8: Case study: interpretability of fuzzy systems applied to identity verification.- Chapte
- r9: Concluding remarks and future perspectives.- Index.
- (source: Nielsen Book Data)
- Publisher's summary
-
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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
- Publication date
- 2017
- Series
- Studies in computational intelligence, 1860-949X ; volume 684
- ISBN
- 9783319528816 (electronic bk.)
- 3319528815 (electronic bk.)
- 3319528807
- 9783319528809
- 9783319528809 (print)
- DOI
- 10.1007/978-3-319-52881-6
