COMPARING WITH THE RESULTS OF SHORT-TERM PRICE FORECASTING FOR CABBAGE BY DIFFERENT OPTIMIZATION ALGORITHM OF CHAOS-RBF NEURAL NETWORK MODEL
CUI Liguo, LI Zhemin
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Agricultural Information Institute of Chinese Academy of Agricultural Science, Beijing 100081; Key Laboratory of Digital Agricultural Early-warning Technology, Ministry of Agriculture,Beijing 100081
Based on the daily retail price of cabbages from January 21, 2008 to May 13, 2012, this paper tries to establish a general chaos-RBF neural network model, a chaos-RBF neural network model based on Genetic Algorithm optimization, and a chaos-RBF Neural network model based on Particle Swarm Optimization. This paper designs these three models by combining the advantages of chaos theory and neural network technology in the processing of the nonlinear problems. We compare the prediction accuracy of different optimization algorithms in the short-term price prediction of cabbages. The conclusion is that the chaos-RBF Neural network model based on Particle Swarm Optimization is significantly better than the other two kinds of chaos-RBF model in terms of the model fitting effect and the prediction accuracy. The result also shows that the chaotic neural network technology has a broad prospect of application in the field of short-term price prediction of cabbages.
CUI Liguo, LI Zhemin.
COMPARING WITH THE RESULTS OF SHORT-TERM PRICE FORECASTING FOR CABBAGE BY DIFFERENT OPTIMIZATION ALGORITHM OF CHAOS-RBF NEURAL NETWORK MODEL. Journal of Systems Science and Mathematical Sciences, 2013, 33(1): 45-54 https://doi.org/10.12341/jssms12018