模糊集合论及其应用（第四版 英文版）
出版时间：2011年版
内容简介
Since its inception 20 years ago， the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found， for example， in artificial intelligence， computer science， control engineering， deci-sion theory， expert systems， logic， management science， operations research，pattern recognition， and robotics. Theoretical advances have been made in many directions. In fact it is extremely difficult for a newcomer to the field or for some-body who wants to apply fuzzy set theory to his problems to recognize properly the present "state of the art." Therefore， many applications use fuzzy set theory on a much more elementary level than appropriate and necessary. On the other hand， theoretical publications are already so specialized and assume such a back-ground in fuzzy set theory that they are hard to understand. The more than 4，000 publications that exist in the field are widely scattered over many areas and in many journals. Existing books are edited volumes containing specialized contri-butions or monographs that focus only on specific areas of fuzzy sets， such as pattern recognition [Bezdek 1981]， switching functions [Kandel and Lee 1979]，or decision making [Kickert 1978]. Even the excellent survey book by Dubois and Prade [1980a] is primarly intended as a research compendium for insiders rather than an introduction to fuzzy set theory or a textbook. This lack of a com-prehensive and modern text is particularly recognized by newcomers to the field and bv those who want to teach fuzzy set theory and its applications.
目录
list of figures
list of tables
foreword
preface
preface to the fourth edition
1 introduction to fuzzy sets
1.1 crispness, vagueness, fuzziness, uncertainty
1.2 fuzzy set theory
part i: fuzzy mathematics
2 fuzzy sets--basic definitions
2.1 basic definitions
2.2 basic set-theoretic operations for fuzzy sets
3 extensions
3.1 types of fuzzy sets
3.2 further operations on fuzzy sets
3.2.1 algebraic operations
3.2.2 set-theoretic operations
3.2.3 criteria for selecting appropriate aggregationoperators
4 fuzzy measures and measures of fuzziness
4.1 fuzzy measures
4.2 measures of fuzziness
5 the extension principle and applications
5.1 the extension principle
5.2 operations for type 2 fuzzy sets
5.3 algebraic operations with fuzzy numbers
5.3.1 special extended operations
5.3.2 extended operations for lr-representation of fuzzy sets
6 fuzzy relations and fuzzy graphs
6.1 fuzzy relations on sets and fuzzy sets
6.1.1 compositions of fuzzy relations
6.1.2 properties of the min-max composition
8.2 fuzzy graphs
6.3 special fuzzy relations
7 fuzzy analysis
7.1 fuzzy functions on fuzzy sets
7.2 extrema of fuzzy functions
7.3 integration of fuzzy functions
7.3.1 integration of a fuzzy function over a crisp interval
7.3.2 integration of a (crisp) real-valued function over a fuzzyinterval
7.4 fuzzy differentiation
8 uncertainty modeling
8.1 application-oriented modeling of uncertainty
8.1.1 causes of uncertainty
8.1.2 type of available information
8.1.3 uncertainty methods
8.1.4 uncertainty theories as transformers of information
8.1.5 matching uncertainty theory and uncertain phenomena
8.2 possibility theory
8.2.1 fuzzy sets and possibility distributions
8.2.2 possibility and necessity measures
8.3 probability of fuzzy events
8.3.1 probability of a fuzzy event as a scalar
8.3.2 probability of a fuzzy event as a fuzzy set
8.4 possibility vs. probability
part ii: applications of fuzzy set theory
9 fuzzy logic and approximate reasoning
9.1 linguistic variables
9.2 fuzzy logic
9.2.1 classical logics revisited
9.2.2 linguistic truth tables
9.3 approximate and plausible reasoning
9.4 fuzzy languages
9.5 support logic programming and fril
9.5.1 introduction
9.5.2 fril rules
9.5.3 inference methods in fril
9.5.4 fril inference for a single rule
9.5.5 multiple rule case
9.5.6 interval and point semantic unification
9.5.7 least prejudiced distribution and learning
9.5.8 applications of fril
10 fuzzy sets and expert systems
10.1 introduction to expert systems
10.2 uncertainty modeling in expert systems
10.3 applications
11 fuzzy control
11.1 origin and objective
11.2 automatic control
11.3 the fuzzy controller
11.4 types of fuzzy controllers
11.4.1 the mamdani controller
11.4.2 defuzzification
11.4.3 the sugeno controller
11.5 design parameters
11.5.1 scaling factors
11.5.2 fuzzy sets
11.5.3 rules
11.6 adaptive fuzzy control
11.7 applications
11.7.1 crane control
11.7.2 control of a model car
11.7.3 control of a diesel engine
11.7.4 fuzzy control of a cement kiln
11.8 tools
11.9 stability
11.10 extensions
12 fuzzy data bases and queries
12.1 introduction
12.2 fuzzy relational databases
12.3 fuzzy queries in crisp databases
13 fuzzy data analysis
13.1 introduction
13.2 methods for fuzzy data analysis
13.2.1 algorithmic approaches
13.2.2 knowledge-based approaches
13.2.3 neural net approaches
13.3 dynamic fuzzy data analysis
13.3.1 problem description
13.3.2 similarity of functions
13.3.3 approaches for analysic dynamic systems
13.4 tools for fuzzy data analysis
13.4.1 requirements for fda tools
13.4.2 data engine
13.5 applications of fda
13.5.1 maintenance management in petrochemical plants
13.5.2 acoustic quality control
14 decision making in fuzzy environments
14.1 fuzzy decisions
14.2 fuzzy linear programming
14.2.1 symmetric fuzzy lp
14.2.2 fuzzy lp with crisp objective function
14.3 fuzzy dynamic programming
14.3.1 fuzzy dynamic programming with crisp state transformationfunction
14.4 fuzzy multicriteria analysis
14.4.1 multi objective decision making (modm)
14.4.2 multi attributive decision making (madm)
15 applications of fuzzy sets in engineering and management
15.1 introduction
15.2 engineering applications
15.2.1 linguistic evaluation and ranking of machine tools
15.2.2 fault detection in gearboxes
15.3 applications in management
15.3.1 a discrete location model
15.3.2 fuzzy set models in logistics
15.3.2.1 fuzzy approach to the transportation problem
15.3.2.2 fuzzy linear programming in logistics
15.3.3 fuzzy sets in scheduling
15.3.3.1 job-shop scheduling with expert systems
15.3.3.2 a method to control flexible manufacturing systems
15.3.3.3 aggregate production and inventory planning
15.3.3.4 fuzzy mathematical programming for maintenancescheduling
15.3.3.5 scheduling courses, instructors, and classrooms
15.3.4 fuzzy set models in inventory control
15.3.5 fuzzy sets in marketing
15.3.5.1 customer segmentation in banking and finance
15.3.5.2 bank customer segmentation based on customerbehavior
16 empirical research in fuzzy set theory
16.1 formal theories vs. factual theories vs. decisiontechnologies
16.1.1 models in operations research and management science
16.1.2 testing factual models
16.2 empirical research on membership functions
16.2.1 type a-membership model
16.2.2 type b-membership model
16.3 empirical research on aggregators
16.4 conclusions
17 future perspectives
abbreviations of frequently cited journals
bibliography
index
