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In this paper introduces the optimization of ensemble neural networks with fuzzy integration type-1 and type-2 for application of the prediction of complex time series, the methods used for optimization are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimization of ensemble neural networks for integration of network response is made with typo-1 and type-2 fuzzy systems. The...
We describe in this paper the architecture of a modular neural network (MNN) for pattern recognition. More recently, the study of modular neural network techniques theory has been receiving significant attention. The design of a recognition system also requires careful attention. The paper aims to use the Ant Colony paradigm to optimize the architecture of this Modular Neural Network for pattern recognition...
In this paper two bio-inspired methods are used to optimize the type-2 fuzzy inference system integrator in an ensemble of three neural networks with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the type-2 fuzzy system integrators that work in response integration of the ensemble neural network for obtaining the final output. In this work an optimized...
In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis...
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