An Application of PSO-RBF Neural Network in Karst Area

被引：0

作者：

Cao, Zhangjun ^{[1
]}

Wang, Dong ^{[1
]}

机构：

[1] Nanjing Univ, Dept Hydro Sci, Sch Earth Sci & Engn, State Key Lab Pollut Control & Resource Reuse, Nanjing 210093, Jiangsu, Peoples R China

来源：

INNOVATIVE THEORIES AND METHODS FOR RISK ANALYSIS AND CRISIS RESPONSE | 2012年 / 21卷

关键词：

Karst area; PSO; RBF neural network; runoff series prediction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The runoff process is much more complicated in the Karst area than that in the other areas. The process is so important that it plays a key role in the risk assessment and disaster prevention in this area. In this paper, runoff series measured by the station are concentrated and a new mathematical method, PSO-RBF neural network is introduced. Both the PSO-RBF and RBF neural network are adopted to predict the 1999's monthly runoff series. And then compare the two models with each other. The comparison shows that the PSO-RBF model performs much better than the one without optimization.

引用

页码：646 / 650

页数：5