STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS
Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency tbr the network to transform to an issue of local solution,a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP,that is,the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights,trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.
作 者： WU Jian-sheng JIN Long 作者单位： WU Jian-sheng(Department of Mathematic and Computer Sciences,Liuzhou Teachers College,Liuzhou 545004 China)JIN Long(Guangxi Research Institute of Meteorological Disasters Mitigation,Naning 530022 China)
刊 名： 热带气象学报(英文版) 英文刊名： JOURNAL OF TROPICAL METEOROLOGY 年，卷(期)： 2009 15(1) 分类号： P456.7 关键词： neural network ensemble particle swarm optimization optimal combination