Overview
- Original research devoted to the resolution of the direct and inverse problems of fuzzy inference with the use of genetic and neural algorithms
- They described a model to solve inverse problems of fuzzy inference and present some applications of this model
- Written by leading experts in the field
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 275)
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Table of contents (9 chapters)
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Front Matter
Keywords
About this book
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving.
The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fuzzy relations and fuzzy rules. Chapter 8 presents a method for extracting fuzzy relations from data. All the algorithms presented in Chapters 2-8 are validated by computer experiments and illustrated by solving medical and technical forecasting and diagnosis problems. Finally, Chapter 9 includes applications of the proposed methodology in dynamic and inventory control systems, prediction of results of football games, decision making in road accident investigations, project management and reliability analysis.Â
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Authors and Affiliations
Bibliographic Information
Book Title: Fuzzy Evidence in Identification, Forecasting and Diagnosis
Authors: Alexander P. Rotshtein, Hanna B. Rakytyanska
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-642-25786-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag GmbH Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-25785-8Published: 27 January 2012
Softcover ISBN: 978-3-642-44421-0Published: 22 February 2014
eBook ISBN: 978-3-642-25786-5Published: 26 January 2012
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XIV, 313
Topics: Computational Intelligence, Simulation and Modeling, Pattern Recognition