Abstract
RBF neural network is a kind of forward neural network model with good perform, it has the best approximation performance. Using improved PSO algorithm which is proposed in this paper, the neural network parameters are optimized, the training method is quick and easy operation. Taking the Chinese cabbage price change trend of Qingdao south village wholesale market in Shandong province as example, and discuss the way to solve the problem by adopting RBF neural network. Constructing time interval unified time series data and normalized processing. Through the training network model to realized the simulation and forecast of price trend. The experimental results show that the model has fast calculation speed and the forecast precision is high.
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Acknowledgments
Project supported by Natural Science Foundation of Shandong Province, China (ZR2011FL016).
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Xu, Q., Liu, M. (2013). Simulation and Forecast About Vegetable Prices Based on PSO-RBFNN Model. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_27
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DOI: https://doi.org/10.1007/978-3-642-38524-7_27
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