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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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Journal Article

Full Parameterization Process for Singleton Fuzzy Logic Controllers: A Computing Algorithm

TL;DR: The presented methodology is very helpful in developing a universal computing algorithm for a standard fuzzy like PID controllers and an illustrative example shows the simplicity of applying the new paradigm.
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Feature Based Fuzzy Rule Base Design for Image Extraction

TL;DR: This work proposes a feature based fuzzy rule guided novel technique that is functionally devoid of any external intervention during execution and takes recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR) to justify the supremacy of performance of this technique in respect of its competitors.
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Pumping-rate control for contaminated groundwater with VOCs by using fuzzy inference model

TL;DR: In this article, a new method is proposed for the remediation of contaminated groundwater based on fuzzy inference and risk evaluation, which can be used to notify residents of the human health risk.
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Sublime Experience: New Strategies for Measuring the Aesthetic Impact of the Sublime

TL;DR: In this article, a software agent based on an Artificial Immune System with a Fuzzy Logic engine is applied to get a model to iden tify the sublime emotion.
Proceedings ArticleDOI

Fuzzy programs based on context of situation

TL;DR: A new framework for interpreting linguistic labels in executing fuzzy programs is proposed and the authors discuss the issues of fuzzy programs through a practical example: a "GO" game playing system.
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.
Journal ArticleDOI

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.