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Books > Computing & IT > Applications of computing > Artificial intelligence
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Fuzzy Evidence in Identification, Forecasting and Diagnosis (Hardcover, 2012 ed.)
Loot Price: R4,388
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Fuzzy Evidence in Identification, Forecasting and Diagnosis (Hardcover, 2012 ed.)
Series: Studies in Fuzziness and Soft Computing, 275
Expected to ship within 10 - 15 working days
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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.
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