动态学习率神经网络预测气温的尝试

Daily Air Temperature Predication Using a Dynamic Learning Rate Neural Network System

摘要:

采用单站气象资料建立动态学习率的神经网络预测模型,进行逐日气温模拟预测,并与逐步回归预测模型和固定学习率的神经网络模型比较,结果表明,神经网络模型预测能力较好,但训练时间长。采用动态学习率的网络模型在不损失预测精度的前提下大大减少了训练时间,为神经网络在气象中的应用提供了一种方法。

关键词:神经网络;BP算法;气温预测;逐步回归

Abstract:

This paper discusses the construction and application of BP-type multilevel mapping model with dynamic learning rateηBased on the 2002 observed temperature data of Yuanjiang Meterorological Station in Hunan Province,daily mean temperature prediction is made by using a dynamic learning rate model,a fixed learning rate model and a stepwise regression model.Results suggest that the BP neural network model is superior to the regression model in predictions,and also swift in learning if the dynamic learning rate is adopted.

Key words:artificial neural networks;BP algorithm;air temperature prediction;stepwise regression