混沌局域法与神经网络组合供水量预测

城市供水量是非线性、非平稳时间序列,组合预测模型能获得更高精度预测结果。通过深入分析混沌局域法与神经网络预测模型特点,提出了一种新的组合预测模型。首先,应用混沌局域法对城市日供水量进行初预测,然后,应用神经网络对预测结果进行修正。由于所提出的组合模型利用了混沌局域法及神经网络进行优势互补,能同时提高预测精度与计算效率。为验证所提出组合预测模型的可行性,采用某市7a实测供水量数据,对混沌局域法、BPNN、RBF及GRNN神经网络4种单一预测模型及相应的3种组合模型预测精度进行定量分析,结果表明,组合预测模型精度都高于对应单一预测模型,混沌局域法与GRNN神经网络组合模型预测精度最高,且运算时间远低于单一神经网络模型运算时间。

Water supply forecasting based on the combination of chaotic local-region method and neural network

Urban water supply is a nonlinear and non-stationary time series, and the combination forecasting model can get more accurate results. Through in-depth analysis of chaotic local-region method and neural network prediction model, this paper puts forward a new combination forecasting model, which uses chaotic local-region method to make a preliminary forecast for urban daily water supply, and then the prediction result is updated by neural network. The proposed combined model makes use of complementary advantages of the chaotic local-region method and the neural network, improving synchronously the accuracy and computational efficiency of the prediction results. To verify the proposed model, the prediction accuracy of the four single prediction models of Chaotic local-region method,BPNN, RBF and GRNN neural network and three corresponding combined models are analyzed quantitatively using seven years water supply data. The results show that combination forecasting model is of higher accuracy than single prediction model, and chaotic local-region method plus GRNN neural network combination model has highest accuracy with much lower computation time than single neural network predication model.