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On fuzzy algorithms

Lotfi A. Zadeh
- pp 127-147
TLDR
A fuzzy algorithm is introduced which, though fuzzy rather than precise in nature, may eventually prove to be of use in a wide variety of problems relating to information processing, control, pattern recognition, system identification, artificial intelligence and, more generally, decision processes involving incomplete or uncertain data.
Abstract
Unlike most papers in Information and Control, our note contains no theorems and no proofs. Essentially, its purpose is to introduce a basic concept which, though fuzzy rather than precise in nature, may eventually prove to be of use in a wide variety of problems relating to information processing, control, pattern recognition, system identification, artificial intelligence and, more generally, decision processes involving incomplete or uncertain data. The concept in question will be called a fuzzy algorithm because it may be viewed as a generalization, through the process of fuzzification, of the conventional (nonfuzzy) conception of an algorithm. More specifically, unlike a nonfuzzy deterministic or nondeterministic algorithm (Floyd, 1967), a fuzzy algorithm may contain fuzzy statements, that is, statements containing names of fuzzy sets (Zadeh, 1965), by which we mean classes in which there may be grades of membership intermediate between full membership and nonmembership. To illustrate, fuzzy algorithms may contain fuzzy instructions such as:

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Citations
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A Review on Classical and Fuzzy PID Controllers

TL;DR: An attempt is made to present the history of the development of classical PID controllers and their enhancement using fuzzy logic theory and a survey of classical and fuzzy PID controllers is presented.
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Computational intelligence approaches for pattern discovery in biological systems.

TL;DR: This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.
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Generalized defuzzification strategies and their parameter learning procedures

TL;DR: Two new objectivedefuzzification strategies are introduced, Gaussian distribution transformation-based defuzzification (GTD) and polynomial transformation- based defuzzifying (PTD), which are based on a discrete universe of discourse and can perform better than the existing strategies.
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Application of Fuzzy Logic for Fault Isolation of Jet Engines

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Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review

TL;DR: This study reviews the common practices and procedures conducted to identify the cost drivers that the past literature has classified into two main categories: qualitative and quantitative drivers.
References
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Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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On Computable Numbers, with an Application to the Entscheidungsproblem

TL;DR: This chapter discusses the application of the diagonal process of the universal computing machine, which automates the calculation of circle and circle-free numbers.
Journal ArticleDOI

L-fuzzy sets

TL;DR: This paper explores the foundations of, generalizes, and continues the work of Zadeh in [I] and [2].
Journal ArticleDOI

Nondeterministic Algorithms

TL;DR: Algorithms to solve combinatorial search problems by using multiple-valued functions are illustrated with algorithms to find all solutions to the eight queens problem on the chessboard, and to finding all simple cycles in a network.