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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 neuro-fuzzy identification of non-linear transient systems: Application to a pilot refrigeration plant

TL;DR: Empirical models that represent the non-linear dynamics of a pilot refrigeration system (chiller) by using identification techniques of neuro-fuzzy systems (ANFIS) will be especially useful in the development of different non- linear control strategies, such as: fuzzy, neuro- fuzzy and model predictive control.
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Improving the efficacy of the lean index through the quantification of qualitative lean metrics

TL;DR: An appraisal is done on techniques for quantifying qualitative lean metrics so that the lean index is a hybrid of both, increasing the confidence in the information derived using theLean index.
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An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

TL;DR: This work gives an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data and compares them in terms of accuracy and interpretability, and argues about their scalability.
Journal ArticleDOI

Design of fuzzy systems with fuzzy flip-flops

TL;DR: The paper introduces a design methodology for developing fuzzy systems with the aid of fuzzy J K flip-flops and detailed design algorithms that essentially exploit a parametric learning of the flip- flops are studied.
Journal ArticleDOI

Optimal fuzzy sliding-mode control for bio-microfluidic manipulation

TL;DR: In this article, an optimal fuzzy sliding-mode control (OFSMC) based on the 8051 microprocessor is designed and a complete microfluidic manipulated biochip system is implemented in this study, with a pneumatic pumping actuator, two feedback-signal photodiodes and flowmeters.
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.
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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.