Displaying similar documents to “Hard and fuzzy classification within the framework of hierarchical and optimization clustering”

Towards a linguistic description of dependencies in data

Ildar Batyrshin, Michael Wagenknecht (2002)

International Journal of Applied Mathematics and Computer Science

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The problem of a linguistic description of dependencies in data by a set of rules R_k: “If X is T_k then Y is S_k” is considered, where T_k’s are linguistic terms like SMALL, BETWEEN 5 AND 7 describing some fuzzy intervals A_k. S_k’s are linguistic terms like DECREASING and QUICKLY INCREASING describing the slopes p_k of linear functions y_k = p_{k}x + q_k approximating data on A_k. The decision of this problem is obtained as a result of a fuzzy partition of the domain X on fuzzy intervals...

Fuzzy termination criteria in Knapsack Problem algorithms.

José Luis Verdegay, Edmundo Vergara-Moreno (2000)

Mathware and Soft Computing

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Fuzzy rule based termination criteria are introduced in two conventional and exact algorithms solving Knapsack Problems. As a consequence two new solution algorithms are obtained. These algorithms are heuristic ones with a high performance. The efficiency of the algorithms obtained is illustrated by solving some numerical examples.

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.

ε-partitions and α-equivalences.

Susana Montes, J. Jiménez, Pedro Gil (1998)

Mathware and Soft Computing

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The aim of this paper is to study a special type of fuzzy relations, the α-equivalences, as well as to consider the relation that connects these with the family of ε-partitions of the referential. Some classic equivalences between set, partitions and fuzzy relations are also studied.

Learning fuzzy systems. An objective function-approach.

Frank Höppner, Frank Klawonn (2004)

Mathware and Soft Computing

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One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system autornatically from data. In this paper, an objective function-based approach to learn fuzzy systems is developed, taking these constraints explicitly into account. Starting...