基于遗传算法的神经网络短期气候预测模型

用遗传算法优化神经网络的连接权和网络结构，并在遗传进化过程中采取保留最佳个体的方法，进行短期气候预测建模研究。该方法克服了由于神经网络初始权值的随机性和网络结构确定过程中所带来的网络振荡，以及网络极易陷入局部解问题。作为应用实例，以广西全区4月份平均降水作为预报量及前期500hPa月平均高度场，海温场高相关区作为预报因子，建立基于遗传算法的神经网络短期气候预测模型。将这种方法与传统的逐步回归方法作对比分析，结果表明，该方法具有预报精度高，稳定性好的特点。

Short-Term Climate Prediction Model of Neural Network Based on Genetic Algorithms

To set up the short-term climate prediction model in this paper, both the neural network and connection weights of genetic algorithm are optimized, the best individual in evolution process is reserved. Thus it can overcome the defects of unstability of neutral network initial weight and falling easily into local solution. As the applied example of a short-time climate forecast model, April mean precipitation in Guangxi area is taken as the predictand, the antecedent montly mean 500 hPa potential field and sea surface temperature filed in some high correlation areas are taken as the pedictors. Predictive performance between the new model and linear regression model for same predictors is discussed based on the independent samples. Results show that the model is superior in prediction accuracy and stability compared with the traditional method.