RBF Neural Network model based on Improved PSO for Predicting River Runoff

摘要：

Based on the observed river runoff data obtained from Yichang hydrological station in the middle of the Yangtze River, Radial Basis Function neural network (RBF) based on improved particle swarm optimization (PSO) was applied to predict river runoff in the Yangtze River. The capacity of solving nonlinear problems is enhanced effectively through adjusting inertia factor dynamically in the algorithm of particle swarm optimization. Improved PSO is applied to optimize the parameters of the neural network and overcome the over-fitting problem and a faster convergence rate is reached. MATLAB was applied to simulate the model The theoretical analysis and simulations show that the prediction model is more practical and has better generalization performance and prediction accuracy than the traditional one.

关键词： River Runoff Improved Particle Swarm Optimization RBF Neural Network Prediction Model

作者: Guo Wenxian Wang Hongxiang Xu Jianxin Zhang Yunfeng

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: 2138-2141

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）