基于自组织算法的改进型GAANN预测模型

Abstract

Abstract: Based on traditional artificial neural network using Genetic Algorithm(GA),this paper introduces a further improved method applying two independent training sets to train the network in order to optimize the neural network structure.Aiming at the characteristics of neural network structure,a model using a hybrid GMDH and artificial neural network is established.It can make the selection of input-lay units easily and improve the ability of rate of studying and the adaptability of neural network.Finally a case implementing this model in analyzing and predicting the grain production of China is presented,and the result indicates that the combined model is an effective way to improve forecasting accuracy.

Key words: self-organizing, Genetic Algorithm, Artificial Neural Network, forecasting of grain production

CLC Number:

C931

ZOU Hao-fei, XIA Guo-ping, YANG Fang-ting. Forecasting Model Using a Hybrid GMDH and Improved Arithmetic of Neural Network Based on Genetic Algorithm[J]. Chinese Journal of Management Science, 2005, (6): 75-80.

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Forecasting Model Using a Hybrid GMDH and Improved Arithmetic of Neural Network Based on Genetic Algorithm

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