| 书目名称 | Fuzzy Evidence in Identification, Forecasting and Diagnosis | | 编辑 | Alexander P. Rotshtein,Hanna B. Rakytyanska | | 视频video | | | 概述 | Original research devoted to the resolution of the direct and inverse problems of fuzzy inference with the use of genetic and neural algorithms.They described a model to solve inverse problems of fuzz | | 丛书名称 | Studies in Fuzziness and Soft Computing | | 图书封面 |  | | 描述 | .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 i | | 出版日期 | Book 2012 | | 关键词 | Direct Inference; Fuzzy Relational Equations; Fuzzy Relations; Fuzzy Relations Tuning; Fuzzy Rules Tunin | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-25786-5 | | isbn_softcover | 978-3-642-44421-0 | | isbn_ebook | 978-3-642-25786-5Series ISSN 1434-9922 Series E-ISSN 1860-0808 | | issn_series | 1434-9922 | | copyright | Springer-Verlag GmbH Berlin Heidelberg 2012 |
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