Design of interpretable fuzzy systems /

Saved in:

Bibliographic Details
Author / Creator:	Cpałka, Krzysztof, author.
Imprint:	Cham, Switzerland : Springer, 2017.
Description:	1 online resource (xi, 196 pages) : illustrations
Language:	English
Series:	Studies in computational intelligence, 1860-949X ; volume 684 Studies in computational intelligence ; v. 684.
Subject:	Fuzzy systems. Fuzzy logic. Fuzzy sets. Fuzzy logic. Fuzzy sets. Fuzzy systems.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11271634

Hidden Bibliographic Details
ISBN:	9783319528816 3319528815 3319528807 9783319528809 9783319528809
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references and index. Online resource; title from PDF title page (SpringerLink, viewed February 10, 2017).
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
Other form:	Printed edition: 9783319528809
Standard no.:	10.1007/978-3-319-52881-6 10.1007/978-3-319-52

Table of Contents:

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.

Similar Items