General Regression Neural Network Forecasting Model Based on PSO Algorithm in Water Demand

摘要：

There is a complicated non-linear relationship between the factors and water demand. General regression neural network (GRNN) was adopted to model the non-linear relationship in the study. The prediction performance of GRNN can vary considerably depending on smoothing parameter. The optimal smoothing parameter is usually determined empirically based on trial-and-error. Particle swarm optimization (PSO) algorithm, to improve GRNN prediction performance, was employed to optimize GRNN and determine an optimal value of smoothing parameter. At the same time, linear inertia weight and chaos variation operator are presented to improve traditional PSO algorithm searching capacity. GRNN forecasting model based on PSO algorithm was used to water demand in Yellow River Basin. The result shows that, compared with Back propagation based on Genetic algorithm model and GRNN based on Genetic algorithm prediction model, the new prediction model is reasonable.

关键词： general regression neural network the improved particle swarm optimization chaos variation operator linear inertia weight water demand forecasting model

作者: Juan Zhou Kaiyun Yang

作者单位: North China University of Water Conversancy and Hydroelectric PowerZhengzhou, 450011, China North China University of Water Conversancy and Hydroelectric Power

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 51-54

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）