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Fuzzy Evidence in Identification, Forecasting and Diagnosis

  • Alexander P. Rotshtein
  • Hanna B. Rakytyanska

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 275)

Table of contents

  1. Front Matter
    Pages 1-13
  2. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 1-37
  3. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 39-53
  4. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 55-117
  5. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 119-148
  6. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 149-162
  7. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 163-192
  8. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 193-233
  9. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 235-258
  10. Alexander P. Rotshtein, Hanna B. Rakytyanska
    Pages 259-313

About this book

Introduction

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. 

 

 

Keywords

Direct Inference Fuzzy Relational Equations Fuzzy Relations Fuzzy Relations Tuning Fuzzy Rules Tuning Fuzzy Sets Genetic Algorithms Identification Inverse Inference Neural Networks Optimization Rules Extraction

Authors and affiliations

  • Alexander P. Rotshtein
    • 1
  • Hanna B. Rakytyanska
    • 2
  1. 1., Industrial Engineering and Management DeJerusalem College of Technology—Machon LJerusalemIsrael
  2. 2., Soft Ware Design DeptVinnitsa National Technical UniversityVinnitsaUkraine

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-25786-5
  • Copyright Information Springer-Verlag GmbH Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-25785-8
  • Online ISBN 978-3-642-25786-5
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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