Displaying similar documents to “Fuzzy termination criteria in Knapsack Problem algorithms.”

Some practical problems in fuzzy sets-based decision support systems.

Alejandro Sancho-Royo, José Luis Verdegay, Edmundo Vergara-Moreno (1999)

Mathware and Soft Computing

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In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the question, first, of the practical determination of membership functions, second of the management of the fuzziness in some optimisation models, and finally of using fuzzy rules for terminating conventional...

Fuzzy linear programming via simulated annealing

Rita Almeida Ribeiro, Fernando Moura Pires (1999)

Kybernetika

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This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.

Linear optimization with bipolar max-parametric hamacher fuzzy relation equation constraints

Samaneh Aliannezhadi, Ali Abbasi Molai, Behnaz Hedayatfar (2016)

Kybernetika

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In this paper, the linear programming problem subject to the Bipolar Fuzzy Relation Equation (BFRE) constraints with the max-parametric hamacher composition operators is studied. The structure of its feasible domain is investigated and its feasible solution set determined. Some necessary and sufficient conditions are presented for its solution existence. Then the problem is converted to an equivalent programming problem. Some rules are proposed to reduce the dimensions of problem. Under...

A Note on Application of Two-sided Systems of ( max , min ) -Linear Equations and Inequalities to Some Fuzzy Set Problems

Karel Zimmermann (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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The aim of this short contribution is to point out some applications of systems of so called two-sided ( max , min ) -linear systems of equations and inequalities of [Gavalec, M., Zimmermann, K.: Solving systems of two-sided (max,min)-linear equations Kybernetika 46 (2010), 405–414.] to solving some fuzzy set multiple fuzzy goal problems. The paper describes one approach to formulating and solving multiple fuzzy goal problems. The fuzzy goals are given as fuzzy sets and we look for a fuzzy set, the...

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

Alina Constantinescu (2007)

Applications of Mathematics

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In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.

Refinement of a fuzzy control rule set.

Antonio González, Raúl Pérez (1998)

Mathware and Soft Computing

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Fuzzy logic controller performance depends on the fuzzy control rule set. This set can be obtained either by an expert or from a learning algorithm through a set of examples. Recently, we have developed SLAVE an inductive learning algorithm capable of identifying fuzzy systems. The refinement of the rules proposed by SLAVE (or by an expert) can be very important in order to improve the accuracy of the model and in order to simplify the description of the system. The refinement algorithm...

Application of fuzzy techniques to the design of algorithms in computer vision.

Eduard Montseny, Pilar Sobrevilla (1998)

Mathware and Soft Computing

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In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the analysis rules, a fuzzy set which will be obtained by applying an aggregation function which will...