Displaying similar documents to “Rule weights in a neuro-fuzzy system with a hierarchical domain partition”

An elemental processor of fuzzy SQL.

Juan Miguel Medina, Olga Pons, M. Amparo Vila (1994)

Mathware and Soft Computing

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This paper reports an alternative for implementing an SQL fuzzy extension on a Fuzzy Relational Database System. This proposal tries to build an FSQL processor using representation and manipulation mechanisms offered by the RDBMS which operates as a host. For this purpose, we are based our work on the formulation of a theoretical model of Fuzzy Relational Databases, on the adoption of a scheme for the representation and implementation of fuzzy information on conventional RDBMS, and on...

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.

Analyzing the reasoning mechanisms in fuzzy rule based classification systems.

Oscar Cordón, María José del Jesús, Francisco Herrera (1998)

Mathware and Soft Computing

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Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we do not consider the information provided by the other rules that are also compatible (have also been fired) with this example. In this paper we analyze this problem and propose...

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.

Evolution-fuzzy rule based system with parameterized consequences

Piotr Czekalski (2006)

International Journal of Applied Mathematics and Computer Science

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While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps:...

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...

A fuzzy logic approach to assembly line balancing.

Daniel J. Fonseca, C. L. Guest, Matthew Elam, Charles L. Karr (2005)

Mathware and Soft Computing

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This paper deals with the use of fuzzy set theory as a viable alternative method for modelling and solving the stochastic assembly line balancing problem. Variability and uncertainty in the assembly line balancing problem has traditionally been modelled through the use of statistical distributions. This may not be feasible in cases where no historical data exists. Fuzzy set theory allows for the consideration of the ambiguity involved in assigning processing and cycle times and the uncertainty...