基于递阶遗传算法和BP网络的时间序列预测

Time Series Forecasting Based on Hierarchical Genetic Algorithm and BP Neural Network

提出一种基于递阶遗传算法和BP神经网络的时间序列预测模型。现有的BP训练方法只能训练BP网络的权重,网络的结构得预先用某种方法确定。利用很好设计的递阶遗传算法能够把网络的结构和权重同时通过训练确定。以铁路客运市场数据进行训练和测试,与传统的BP网络预测模型相比较,结果证明该模型的预测精确度是令人满意的,所提出的方法是可行的。

A time series forecasting model, based on hierarchical genetic algorithm and BP neural network, was proposed. Different from the existing BP training method that can only lead to determination of connection weights, a well-designed hierarchical genetic algorithm was used to train BP neural network with both connection weights and numbers of neurons in a hidden layer determined at the same time. The model was then used to forecast the market of railway passenger traffic. It is shown that the model based on hierarchical genetic algorithm and BP neural network is simple and effective.