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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 ArticleDOI

On a class of fuzzy computable functions

TL;DR: It is proved that FR is a strict subset of the class of W-functions with recursive graph, as defined by Gerla, and it is shown that it is possible to generate the class FR by means of programs written in the language XL.
Proceedings ArticleDOI

Metrics to Evaluate a Monitor Agile Based Software Development Projects - A Fuzzy Logic Approach

TL;DR: This paper will be a short description of GQM & PSM methodologies, focused on selecting a number of "sensible" metrics in Agile based software development context, and will be concentrated on the fuzzy set theory, fuzzy logic and their implementation on the selected metrics in order to evaluate & monitor a general agile basedSoftware development project.
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Analysis of fuzzy logic methods for forecasting customer churn

TL;DR: The main assumption of the study is the hypothesis that the use of a fuzzy neural network formed on the basis of fuzzy logic algorithms can improve the accuracy of predicting customer churn relative to available solutions.
Journal ArticleDOI

Dynamic portfolio insurance strategy: a robust machine learning approach*

TL;DR: The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and the model’s robustness, and the proposed strategy outperforms the previous model.
Proceedings ArticleDOI

A DSmT based combination scheme for multi-class classification

TL;DR: An inspired version of Appriou's model for estimating the generalized basic belief assignments is presented, which allows decomposing a n-class problem into a series of n-combination, while providing n-calibrated outputs into the multi-class framework.
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.