Predicting the Emergency Logistics Demand for Tropical Cyclone Based on Improved RBF Neural Network
Considering the advantages and limitations of traditional RBF in prediction, an improved BRF based on ant colony clustering algorithm is introduced in this paper. This method is used to predict the demand of emergency resources. Because of artificial neural network algorithms advantages, such as strong learning ability and non-linear aproximation of various levels of accuracy, this method is widely adopted in prediction. A RBF network is developed in this paper by adding ant colony clustering algorithm with can search in the local areas. And the weights of the connections between hidden layer and output layer are desinged by linear approach base on the expected outputs of the hidden and output layers. Therefore, the approach in predicting the demand of emergency logistics is established. And a case study has been made to show the validity of this approach, where this method was adopted in predicting the demand of emergency logistics in anti-tropical cyclone disasters. The experimental studies were conducted by MATLAB.
Emergency Logistics Demand prediction Ant Colony Clustering Algorithm Radical Basis Function Neural Network Tropical Cyclone Disaster
Bin Yang Jian Zhang Lei Zhao
Logistics Research Center, Shanghai Maritime University, Shanghai 200135, P. R. China
国际会议
The Tenth International Conference on Information and Management Sciences(IMS)(第十届信息与管理科学国际会议)
拉萨
英文
6-12
2011-08-06（万方平台首次上网日期，不代表论文的发表时间）