Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade-Correlation Neural Network |
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摘 要: | UsingGeneticAlgorithmstoImprovetheSearchoftheWeightSpaceinCascade-CorrelationNeuralNetwork¥E.A.Mayer,K.J.Cios,L.Berke&A.Vary(...
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Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade-Correlation Neural Network |
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Abstract: | In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys. |
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Keywords: | Genetic algorithm Cascade correlation Weight space search Neural network. |
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