Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm

Abstract : In this paper, the theory and construction methods of four models are presented for predicting the vegetable market price, which are BP neural network model, the neural network model based on genetic algorithm, RBF neural network model and an integrated prediction model based on the three models above. The four models are used to predict the Lentinus edodes price for Beijing Xinfadi wholesale market. A total of 84 records collected between 2003 and 2009 were fed into the four models for training and testing. In summary, the predicting ability of BP neural network model is the worst. The neural network model based on genetic algorithm was generally more accurate than RBF neural network model. The integrated prediction model has the best results.
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Communication dans un congrès
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-346 (Part III), pp.672-681, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18354-6_79〉
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Soumis le : lundi 17 juillet 2017 - 17:00:55
Dernière modification le : lundi 17 juillet 2017 - 17:10:05

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Changshou Luo, Qingfeng Wei, Liying Zhou, Junfeng Zhang, Sufen Sun. Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-346 (Part III), pp.672-681, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18354-6_79〉. 〈hal-01563492〉

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