Displaying similar documents to “Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction”

Fuzzy sets in pattern recognition, image analysis and automatic speech recognition

Dwijesh Dutta Majumder (1985)

Aplikace matematiky

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Fuzzy set theory, a recent generalization of classical set theory, has attracted the attention of researchers working in various areas including pattern recognition, which has had a seminal influence in the development of this new theory. This paper attempts to discuss some of the methodologies that have been suggested for pattern recognition, and techniques for image processing and speech recognition.

On classification with missing data using rough-neuro-fuzzy systems

Robert K. Nowicki (2010)

International Journal of Applied Mathematics and Computer Science

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

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...

Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański (2005)

International Journal of Applied Mathematics and Computer Science

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This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty...