PSO-BP Neural Network Model for Predicting Water Temperature in the Middle of the Yangtze River

摘要：

River temperature prediction is an Important project in the environmental impact assessments. Based on river temperature data of Yichang hydrological station in the middle reach of the Yangtze River, BP neural network model based on particle swarm optimization (PSO) was applied to predict river temperature of the Yangtze River. PSO was used to optimize the initial weights of nodes in BP neural network and overcome the over-fitting problem and the local minima problem of the BP neural network. MATLAB was applied to simulate the model. The results show that the prediction precision was improved greatly and the model had better generalization performance. The study proved that PSO-BP neural network model was effective in river temperature prediction.

关键词： Particle Swarm Optimization BP neural network Prediction model River temperature

作者: Guo Wenxian Wang Hongxiang Xu Jianxin Dong Wensheng

作者单位: North China University of Water Resources and Electric Power, Zhengzhou, Henan, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 2121-2124

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）