The Application of Rough Set Neural Networks of GSS-PSO in the Risk Evaluation of Collapse and Rockfall Disasters

摘要：

In this paper,an intelligent prediction approach based on the neural networks rough set of a Genetic Selection Strategy Particle Swarm Optimization Algorithm(GSSPSO)is proposed to measure the risky area caused by slope.With this approach, the attribute reduction method based on neighborhood rough set is adopted to conduct the attribute reduction, then the genetic strategy is used to reform the particle swarm optimization (PSO), and the reformed method will replace the traditional BP algorithm to train the weight and threshold value of the neural networks. Finally the well-trained neural networks will be used to evaluate the risk of collapse and rockfall.The result of simulation indicates that this new approach reduce the complexity of neural networks,save the training and enhances the precision of prediction.

关键词： GSS-PSO rough set neural networks collapse and rockfall

作者: Yong Liu Hongming Yu Ping Zhong

作者单位: Faculty of Engineering,Faculty of Mechanical&Electronic Information,China University of Geosciences, Faculty of Engineering,China University of Geosciences,Wuhan 430074, P.R.China School of Mathematics and Physics,China University of Geosciences,Wuhan 430074, P.R.China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 481-484

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）