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This paper concentrates on studying the use of interval type-2 fuzzy sets for the pattern classification problem. Even though researchers recognize that type-2 fuzzy sets are more difficult to understand and use than type-1 fuzzy sets, the interest in the study is motivated by the additional power to represent uncertainty in different levels. The work developed here relies on the recent advances concerning...
Many high-order systems have a large state space. Such systems need to additional computation time for complex calculation to find the output response. Traditionally, iteration methods have been applied to solve this problem. In this paper advantages of stability equation method derived by Parmer, [1], and the error minimization technique used in genetic-fuzzy algorithm have been combined to propose...
This paper introduces genetic algorithm (GA) based method to construct a satisfactory fuzzy system directly from some gathered input-output data of the discussed problem. In this method, each individual in the population is constructed to determine the number of fuzzy rules and the premise and the consequent part of the fuzzy system. With the special mutation operation and the adequate fitness function,...
Fuzzy logic based methods together with the techniques from Artificial Intelligence have gained importance. Association rules together with fuzzy logic to model the fuzzy association rules are being used for classifying data. These together with the techniques of genetic algorithms like genetic programming are producing better results. Therefore, in this article, we firstly analyze the current situation...
In this paper, an effective genetic algorithm (GA) approach is proposed for tuning the parameters of membership functions based on input-output pairs. By minimizing a quadratic measure of the error in the least-squares sense, the real-valued chromosomes of a population are evolved to get the best coefficients. Comparison to the well-known back-propagation algorithm for fuzzy logic system shows that...
In this paper, we present a fuzzy inventory problem and apply genetic algorithm to solve it. We derive the cost function in the fuzzy sense, and solve the nearly optimal solution by genetic algorithm.
This work provides an effective approach based on adaptive neuro-fuzzy inference system to the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the ML-CFAR (maximum-likelihood CFAR) detector in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The genetic learning algorithm...
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