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.
Chapter PDF
Similar content being viewed by others
References
Amjady, N.: Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Transaction on Power Systems, 887–896 (2006)
Yamashita, T., Hirasawa, K., Hu, J.: Multi-branch neural networks and its application to stock price prediction. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, pp. 1–7. Springer, Heidelberg (2005)
Wei, M.S., Li, G.Y.: FNN-based Intelligent Insect Pest Forecast for Crops. Journal of Taiyuan University of Science and Technology, 442–445 (2007)
Najafi, B., Zibaei, M.: Forecasting price of some crop products in Pars province: application of artificial neural network. Journal of Science and Technology of Agriculture and Natural Resources, 501–512 (2007)
Luo, C.S.: Estimating Root density distribution of winter wheat under Water and salinity stress using the artificial neural network model based on Genetic algorithm. China Agricultural University 20-60 (2002)
Gu, Q.W., Chen, G., Zhu, L.L.: Short-term marginal price forecasting based on genetic algorithm and radial basis function neural network. Power System Technology 25(7), 18–21 (2006)
Wu, C.S., Wu, C., Kang, L.S.: Research on stock price forecasting methods by support vector machines based on genetic algorithms. In: 3rd International Conference on Innovation and Management. Wuhan PR China, pp. 29–30 (2005)
Shao, L., Zhou, X.D.: Industrial water demand forecast in Shanxi province based on RBFN. Yellow River, 53–56 (2010)
Qiang, X.D., Xiao, Q., Luo, H.Y.: Research on prediction of RMB exchange rate based on improved RBF neural network. Computer Engineering and Applications, 229–231 (2010)
Wang, J., Tian, L., Jiang, H.: Forecasting of Short-term Power Load in RBF Network Based on Genetic Algorithm. Electronic Technology 15-1 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 IFIP International Federation for Information Processing
About this paper
Cite this paper
Luo, C., Wei, Q., Zhou, L., Zhang, J., Sun, S. (2011). Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm. In: Li, D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18354-6_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-18354-6_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18353-9
Online ISBN: 978-3-642-18354-6
eBook Packages: Computer ScienceComputer Science (R0)