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Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

Listed author(s):
  • Guo, Qiang
  • Luo, Chang-shou
  • Wei, Qing-feng
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    Considering the complexity of vegetables price forecast, the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm by using the characteristics of genetic algorithm and neural work. Taking mushrooms as an example, the parameters of the model are analyzed through experiment. In the end, the results of genetic algorithm and BP neural network are compared. The results show that the absolute error of prediction data is in the scale of 10%; in the scope that the absolute error in the prediction data is in the scope of 20% and 15%. The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model, especially the absolute error of prediction data is within the scope of 20%. The accuracy of genetic algorithm based on neural network is obviously good than BP neural network model, which represents the favorable generalization capability of the model.

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    Article provided by USA-China Science and Culture Media Corporation in its journal Asian Agricultural Research.

    Volume (Year): 03 (2011)
    Issue (Month): 05 (May)

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    Handle: RePEc:ags:asagre:117430
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