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《Science of Surveying and Mapping》 2008-02
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Optimal selection of prediction parameters for probability-integral method using particle swarm optimization and BP neural network

YU Ning-feng,YANG Hua-chao(School of Environmental Science & Spatial Informatics, China University of Mining & Technology, Jiangsu, Xuzhou221008, China)  
In order to determine the prediction parameters of probability-integral method and to improve the prediction accuracy, a new method by combining particle swarm optimization algorithm (PSO) and BP neural network (PSO-BP) is presented. In this method, an improved hybrid PSO algorithm is used to optimize the connection weights and thresholds values of BP neural network. An optimal selection model for prediction parameters of probability-integral method using this hybrid PSO-BP neural network algorithm is constructed based on the relation analysis between the parameters of probability-integral method and geological mining conditions. Typical data of surface moving observation stations is used as samples. Comparison analysis is addressed between calculated values generated by PSO-BP method and observed values, and the performance of PSO-BP method is also compared with improved BP algorithm mentioned in first bibliography. Results indicate that PSO-BP calculation model has higher convergent speed and higher precision. A new optimal selection model for parameters of probability-integral method is provided.
【Fund】: 国家自然科学基金资助项目(50474046)
【CateGory Index】: O213
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