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Springer Fuzzy Evidence in Identification, Forecasting and Diagnosis

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Description

Fuzzy Evidence in Identification, Forecasting and Diagnosis (Studies in Fuzziness and Soft Computing, 275) provides a specialized methodology for designing and tuning fuzzy expert systems. This technical resource focuses on identifying nonlinear objects by building effective input-output models using both expert and experimental information. The book offers practical applications for solving complex problems through direct and inverse fuzzy evidence. It is designed to help readers navigate the processes of forecasting and diagnosis within various computational frameworks. By utilizing fuzzy sets, genetic algorithms, and neural nets, this text provides the foundational knowledge and advanced techniques required for modern soft computing research. Whether you are studying fuzzy inference or looking to improve model accuracy, this volume serves as a comprehensive guide for understanding how to integrate expert knowledge with experimental data to achieve precise results in identification and diagnosis.

Key Features

Learn a methodology for designing and tuning fuzzy expert systems to identify nonlinear objects effectively.

Build robust input-output models by combining expert knowledge with experimental information.

Apply direct and inverse fuzzy evidence to solve complex forecasting and diagnosis problems.

Gain foundational knowledge in fuzzy sets, genetic algorithms, and neural nets for better understanding.

Explore direct fuzzy inference techniques through structured technical analysis and chapters.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
27 January 2012
Listed Since
29 October 2011

Barcode

No barcode data available

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