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Studies in Fuzziness and Soft Computing Ser.: Fuzzy Evidence in Identification, Forecasting and Diagnosis by Alexander P. Rotshtein and Hanna B. Rakytyanska (2014, Trade Paperback)
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About this product
Product Identifiers
PublisherSpringer Berlin / Heidelberg
ISBN-103642444210
ISBN-139783642444210
eBay Product ID (ePID)206054398
Product Key Features
Number of PagesXiv, 313 Pages
Publication NameFuzzy Evidence in Identification, Forecasting and Diagnosis
LanguageEnglish
Publication Year2014
SubjectProgramming / Algorithms, Engineering (General), Intelligence (Ai) & Semantics, Computer Simulation, Logic, Set Theory, Computer Vision & Pattern Recognition
TypeTextbook
Subject AreaMathematics, Computers, Technology & Engineering
AuthorAlexander P. Rotshtein, Hanna B. Rakytyanska
SeriesStudies in Fuzziness and Soft Computing Ser.
FormatTrade Paperback
Dimensions
Item Weight17.8 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
Dewey Edition23
Series Volume Number275
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal006.33
Table Of ContentPreface.- Fundamentals of intellectual technologies.- Direct inference based on fuzzy rules.- Fuzzy rules tuning for direct inference.- Fuzzy rules extraction from experimental data.- Inverse inference based on fuzzy relational equations.- Inverse inference with fuzzy relations tuning.- Inverse inference based on fuzzy rules.- Fuzzy relations extraction from experimental data.- Applied fuzzy systems.
SynopsisThis book presents original research on fuzzy inference, describes a new model for solving inverse problems of fuzzy inference and presents applications in system control problems, sport forecasting, automobile design, system reliability analysis and more., Preface.- Fundamentals of intellectual technologies.- Direct inference based on fuzzy rules.- Fuzzy rules tuning for direct inference.- Fuzzy rules extraction from experimental data.- Inverse inference based on fuzzy relational equations.- Inverse inference with fuzzy relations tuning.- Inverse inference based on fuzzy rules.- Fuzzy relations extraction from experimental data.- Applied fuzzy systems., 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.
LC Classification NumberQ342
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