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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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Design Controller for a Class of Nonlinear Pendulum Dynamical System

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TL;DR: Examining powder snow avalanches by introducing a predetermined degree of variation, or fuzziness, in model parameters, fuzzy set theory makes it possible to evaluate how the results are affected by a given change in the model parameters.
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A Big Data Approach for the Extraction of Fuzzy Emerging Patterns

TL;DR: Results show that the approach used by BD-EFEP opens new research lines for the extraction of high descriptive emerging patterns in big data environments, and shows the advantages of the algorithm proposed for the emerging pattern mining task inbig data problems.
Proceedings ArticleDOI

A fuzzy theory in the risk assessment and reduction algorithms for a human centered robotics

TL;DR: An extended approach for the risk assessment and reduction is presented which is based on the fuzzy theory application integrating human factor and work conditions ergonomic analysis, adapted for the human robot interaction domain and extends the standardized risk assessment techniques used in the manufacturing and conventional robotics.
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Interactive scheduling in the chemical process industry

TL;DR: SKYE is implemented, an interactive decision-aid tool for human schedulers, which is capable of handling a very large set of scheduling problems in the CPI, and combines stochastic techniques with deductive techniques such as, heuristics, constraint propagation and temporal reasoning.
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.