粒子群-神经网络集成学习算法气象预报建模研究

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

针对BP神经网络在实际气象预报应用中,网络结构难以确定以及网络极易陷入局部解问题,提出一种基于神经网络的粒子群集成学习算法的气象预报模型,以BP算法为基本框架,在学习过程中引入粒子群算法,优化设计神经网络的网络结构和初始连接权,获得一组合适网络结构和初始连接权,再进行新一轮BP神经网络训练,获得一批独立的神经网络个体,以"误差绝对值和最小"为最优准则,采用线性规划方法计算各集成个体的权系数,生成神经网络的输出结论,以此建立短期气候预测模型。以广西的月降水量进行实例分析,计算结果表明该方法学习能力强、泛化性能高,能够有效提高系统预测的准确率。

For the difficulty in deciding on the structure of BP network in real meteorological application and the tendency for 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.