Gold price prediction method based on improved PSO-BP

被引：0
|
作者：
Wang, Yan [1 ]
Zhang, Liguo [2 ]
Liu, Yongfu [2 ]
Guo, Jun [1 ]
机构：
[1] Dept. of computer, North China Electric Power University, Baoding,Hebei,071003, China
[2] College of Information Science & Technology, Agricultural University of Hebei, Baoding,Hebei,071001, China
来源：
International Journal of u- and e- Service, Science and Technology | 2015年 / 8卷 / 11期
关键词：
D O I：
10.14257/ijunesst.2015.8.11.25
中图分类号：
学科分类号：
摘要：
Aimed at the highly nonlinear and uncertainty of gold price changes, a new method for gold price predition based on improved PSO-BP is proposed. By introducing mutation operation and adaptive adjust of inertia weight, the problem of easy to fall into local optimum, premature, low precision and low later interation efficiency of PSO are solved. By using the improved PSO to optimiaze BP neuralnetwork’s parameters, the learning rate and optimization capability of conventional BP are effectively improved. The simulation results of gold price prediction show that the predict accuracy of the new method is significantly higher than that of conventional BP neuralnetwork and wavelet neuralnetwork method. And the method is effective and feasible. © 2015 SERSC.
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页码：253 / 260
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