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Fuzzy Set Theory - and Its Applications

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TLDR
The book updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research.
Abstract
Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

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Journal ArticleDOI

Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach

TL;DR: The proposed linguistic approach is based on fuzzy set theory and Dempster-Shafer theory of evidence, where the later has been used to combine the risk of components to determine the system risk.
Journal ArticleDOI

An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers

TL;DR: An interactive user-friendly microcomputer-based decision support system for consensus reaching processes and a fuzzy-logic-based calculus of linguistically quantified propositions is employed.
Journal ArticleDOI

Fuzzy approach to the environmental impact evaluation

TL;DR: The environmental parameters are defined through fuzzy numbers and through the matrix method, the total environmental impact (T.E.I.) in fuzzy terms is calculated, as in fuzzy form, the percentage of impact on every environmental component is calculated.
Journal ArticleDOI

Data envelopment analysis with missing data: an application to University libraries in Taiwan

TL;DR: The concept of a membership function used in fuzzy set theory for representing imprecise data is adopted and the smallest possible, most possible, and largest possible values of the missing data are derived from the observed data to construct a triangular membership function.
Journal ArticleDOI

Syndromes of Global Change: a qualitative modelling approach to assist global environmental management

TL;DR: It turns out that a mixed policy of combating poverty and introducing soil preserving agricultural techniques and practices is most promising to tackle the Sahel Syndrome dynamics.
References
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Book

Decision-making in a fuzzy environment

TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
Book ChapterDOI

A framework for representing knowledge

Marvin Minsky
TL;DR: The enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts is discussed and it is suggested that networks of frames are a reasonable approach to represent such knowledge.
Journal ArticleDOI

Social Choice and Individual Values.

TL;DR: In this article, the authors present a destination search and find the appropriate manuals for their products, providing you with many Social Choice And Individual Values. You can find the manual you are interested in in printed form or even consider it online.
Book

Principles of Artificial Intelligence

TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Journal ArticleDOI

Fuzzy programming and linear programming with several objective functions

TL;DR: It is shown that solutions obtained by fuzzy linear programming are always efficient solutions and the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution are shown.