Description 
1 online resource (xxii, 526 pages) : illustrations 
Contents 
Preface  1 Introduction  1.1 Digital communities and a fundamental quest for humancentric systems  1.2 A historical overview: towards a nonAristotelian perspective of the world  1.3 Granular Computing  1.4 Quantifying information granularity: generality versus specificity  1.5 Computational Intelligence  1.6 Granular Computing and Computational Intelligence  1.7 Conclusions  Exercises and problems  Historical notes  References  2 Notions and Concepts of Fuzzy Sets  2.1 Sets and fuzzy sets: a departure from the principle of dichotomy  2.2 Interpretation of fuzzy sets  2.3 Membership functions and their motivation  2.4 Fuzzy numbers and intervals  2.5 Linguistic variables  2.6 Conclusions  Exercises and problems  Historical notes  References  3 Characterization of Fuzzy Sets  3.1 A generic characterization of fuzzy sets: some fundamental descriptors  3.2 Equality and inclusion relationships in fuzzy sets  3.3 Energy and entropy measures of fuzziness  3.4 Specificity of fuzzy sets  3.5 Geometric interpretation of sets and fuzzy sets  3.6 Granulation of information  3.7 Characterization of the families of fuzzy sets  3.8 Fuzzy sets, sets, and the representation theorem  3.9 Conclusions  Exercises and problems  Historical notes  References  4 The Design of Fuzzy Sets  4.1 Semantics of fuzzy sets: some general observations  4.2 Fuzzy set as a descriptor of feasible solutions  4.3 Fuzzy set as a descriptor of the notion of typicality  4.4 Membership functions in the visualization of preferences of solutions  4.5 Nonlinear transformation of fuzzy sets  4.6 Vertical and horizontal schemes of membership estimation  4.7 Saaty's priority method of pairwise membership function estimation  4.8 Fuzzy sets as granular representatives of numeric data  4.9 From numeric data to fuzzy sets  4.10 Fuzzy equalization  4.11 Linguistic approximation 

4.12 Design guidelines for the construction of fuzzy sets  4.13 Conclusions  Exercises and problems  Historical notes  References  5 Operations and Aggregations of Fuzzy Sets  5.1 Standard operations on sets and fuzzy sets  5.2 Generic requirements for operations on fuzzy sets  5.3 Triangular norms  5.4 Triangular conorms  5.5 Triangular norms as a general category of logical operators  5.6 Aggregation operations  5.7 Fuzzy measure and integral  5.8 Negations  5.9 Conclusions  Exercises and problems  Historical notes  References  6 Fuzzy Relations  6.1 The concept of relations  6.2 Fuzzy relations  6.3 Properties of the fuzzy relations  6.4 Operations on fuzzy relations  6.5 Cartesian product, projections and cylindrical extension of fuzzy sets  6.6 Reconstruction of fuzzy relations  6.7 Binary fuzzy relations  6.8 Conclusions  Exercises and problems  Historical notes  References  7 Transformations of Fuzzy Sets  7.1 The extension principle  7.2 Compositions of fuzzy relations  7.3 Fuzzy relational equations  7.4 Associative Memories  7.5 Fuzzy numbers and fuzzy arithmetic  7.6 Conclusions  Exercises and problems  Historical notes  References  8 Generalizations and Extensions of Fuzzy Sets  8.1 Fuzzy sets of higher order  8.2 Rough fuzzy sets and fuzzy rough sets  8.3 Intervalvalued fuzzy sets  8.4 Type2 fuzzy sets  8.5 Shadowed sets as a threevalued logic characterization of fuzzy sets  8.6 Probability and fuzzy sets  8.7 Probability of fuzzy events  8.8 Conclusions  Exercises and problems  Historical notes  References  9 Interoperability Aspects of Fuzzy Sets  9.1 Fuzzy set and its family of scuts  9.2 Fuzzy sets and their interfacing with the external world  9.3 Encoding and decoding as an optimization problem of vector quantization  9.4 Decoding of a fuzzy set through a family of fuzzy sets 

9.5 Taxonomy of data in structure description with shadowed sets  9.6 Conclusions  Exercises and problems  Historical notes  References  10. Fuzzy Modeling: Principles and Methodology  10.1 The architectural blueprint of fuzzy models  10.2 Key phases of the development and use of fuzzy models  10.3 Main categories of fuzzy models: an overview  10.4 Verification and validation of fuzzy models  10.5 Conclusions  Exercises and problems  Historical notes  References  11 Rulebased Fuzzy Models  11.1 Fuzzy rules as a vehicle of knowledge representation  11.2 General categories of fuzzy rules and their semantics  11.3 Syntax of fuzzy rules  11.4 Basic Functional Modules: Rule base, Database, and Inference scheme  11.5 Types of RuleBased Systems and Architectures  11.6 Approximation properties of fuzzy rulebased models  11.7 Development of RuleBased Systems  11.8 Parameter estimation procedure for functional rulebased systems  11.9 Design issues of rulebased systems  consistency, completeness, and the curse of dimensionality  11.10 The curse of dimensionality in rulebased systems  11.11 Development scheme of fuzzy rulebased models  11.12 Conclusions  Exercises and problems  Historical notes  References  12 From Logic Expressions to Fuzzy Logic Networks  12.1 Introduction  12.2 Main categories of fuzzy neurons  12.3 Uninormbased fuzzy neurons  12.4 Architectures of logic networks  12.5 The development mechanisms of the fuzzy neural networks  12.6 Interpretation of the fuzzy neural networks  12.7 From fuzzy logic networks to Boolean functions and their minimization through learning  12.8 Interfacing the fuzzy neural network  12.9 Interpretation aspects  a refinement of induced rulebased system  12.10 Reconciliation of perception of information granules and granular mappings  12.11 Conclusions  Exercises and problems  Historical notes 
Summary 
A selfcontained treatment of fuzzy systems engineering, offering conceptual fundamentals, design methodologies, development guidelines, and carefully selected illustrative materialForty years have passed since the birth of fuzzy sets, in which time a wealth of theoretical developments, conceptual pursuits, algorithmic environments, and other applications have emerged. Now, this readerfriendly book presents an uptodate approach to fuzzy systems engineering, covering concepts, design methodologies, and algorithms coupled with interpretation, analysis, and underlying engineering knowledge. The result is a holistic view of fuzzy sets as a fundamental component of computational intelligence and humancentric systems. Throughout the book, the authors emphasize the direct applicability and limitations of the concepts being discussed, and historical and bibliographical notes are included in each chapter to help readers view the developments of fuzzy sets from a broader perspective. A radical departure from current books on the subject, Fuzzy Systems Engineering presents fuzzy sets as an enabling technology whose impact, contributions, and methodology stretch far beyond any specific discipline, making it applicable to researchers and practitioners in engineering, computer science, business, medicine, bioinformatics, and computational biology. Additionally, three appendices and classroomready electronic resources make it an ideal textbook for advanced undergraduate and graduatelevel courses in engineering and science 
Bibliography 
Includes bibliographical references and index 
Notes 
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL 

English 

digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL 
Subject 
Soft computing.


Fuzzy systems.


Fuzzy systems  Mathematical models


Fuzzy sets.


Fuzzy mathematics.


COMPUTERS  Enterprise Applications  Business Intelligence Tools.


COMPUTERS  Intelligence (AI) & Semantics.


Fuzzy systems  Mathematical models


Fuzzy sets


Fuzzy mathematics


Fuzzy systems


Soft computing

Form 
Electronic book

Author 
Gomide, Fernando.

LC no. 
2007001711 
ISBN 
9780470168967 

047016896X 

9780470168950 

0470168951 

1281001929 

9781281001924 

9786611001926 

6611001921 
