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中文摘要：
针对现有需水预测模型进行多周期预测时存在误差随预测周期延长而累积、抗随机因素干扰能力不足等问题,提出动态等维新息径向基神经网络模型,采用聚类方法进行网络学习,并将其应用于东莞市年需水量预测中。结果表明：动态等维新息径向基神经网络模型相对于基本径向基神经网络模型具有更高的预测精度,并且预测误差不会随着预测周期的延长而累积。
英文摘要：
To solve the problems of errors increasing with the extension of the forecast cycle and inadequate protection against random factor interference when using the existing models for multi-cycle urban water demand forecast,an equidimensional information renewal radial basis function neural network model was proposed,and the clustering approach was used for system learning.This model was applied to the annual water demand forecast in Dongguan City.The results show that the model has higher forecast accuracy than the fundamental radial basis function neural network model,and its forecast errors do not increase with the extension of the forecast cycle.
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