2019
DOI: 10.1111/exsy.12383
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Metaheuristic algorithm to train product and sigmoid neural network classifiers

Abstract: This paper develops three frameworks based on a metaheuristic algorithm to train neural network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first methodology spreads a base configuration over the nodes of a computing cluster; each of them executes the same algorithm to train the neural network with a different parameter setting. The second approach does a refined training via a biphase metaheuristic algorithm to maintain the diversity a period longer than the usual; it may b… Show more

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Cited by 2 publications
(2 citation statements)
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“…Network. e gradient learning method presented above enables the network to gradually adjust along the direction of local improvement, which means inappropriate initial weights and factors can increase the possibility of falling into local optimum or even failing because of the complexity and nondifferentiability of the search space [25]. In this regard, the genetic algorithm (GA) with global search capability is employed to improve the model by optimizing the initial weights and factors in this paper.…”
Section: Genetic Algorithm-optimized Wavelet Neuralmentioning
confidence: 99%
“…Network. e gradient learning method presented above enables the network to gradually adjust along the direction of local improvement, which means inappropriate initial weights and factors can increase the possibility of falling into local optimum or even failing because of the complexity and nondifferentiability of the search space [25]. In this regard, the genetic algorithm (GA) with global search capability is employed to improve the model by optimizing the initial weights and factors in this paper.…”
Section: Genetic Algorithm-optimized Wavelet Neuralmentioning
confidence: 99%
“…La primera vía sienta sus bases en que los usuarios de tecnologías dejan un rastro en la red en forma de datos, lo que permite hacer retratos gracias a algoritmos que originan unos patrones. Los macrodatos o Big data capacitan la visualización, recuperación y gestión de grandes volúmenes de datos aplicando la minería de datos u obtención de modelos de predicción como los árboles de decisión o las redes neuronales artificiales, que son entrenadas con el fin de construir un modelo que reproduce el método de aprendizaje del cerebro humano y realiza predicciones mediante algoritmos de patrones de comportamiento y acción (Tallón-Ballesteros, 2019). Esto permite controlar el itinerario de un usuario y parametrizar las emociones o la empatía que produce un producto o una idea en la red.…”
Section: Introductionunclassified