Fuzzy Evidence in Idetification Forecasting and Diagnosis at Meripustak

Fuzzy Evidence in Idetification Forecasting and Diagnosis

Books from same Author: Alexander P Rotshtein

Books from same Publisher: Springer Velage

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  • General Information  
    Author(s)Alexander P Rotshtein
    PublisherSpringer Velage
    ISBN9783642257858
    Pages313
    BindingHardcover
    LanguageEnglish
    Publish YearJanuary 2012

    Description

    Springer Velage Fuzzy Evidence in Idetification Forecasting and Diagnosis by Alexander P Rotshtein

    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.