基于Volterra序列的神经网络及其在自来水流量预测中的应用

Chen Kun, Li Lixiong
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引用次数: 1

摘要

社区自来水系统受多种因素的影响,是一个典型的非线性动态系统。神经网络和Volterra系列在非线性动态系统中有着广泛的应用。讨论了Volterra级数与BP神经网络的关系,提出了基于Volterra级数的神经网络和高阶Volterra级数核的求解方法。本文将ARMA模型、BP神经网络和基于Volterra序列的神经网络应用于社区自来水流量的短期预测。对比结果表明,基于Volterra序列的神经网络优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Volterra Series-based Neural Network and its Application in Tap-water Flow Forcast
The system of community tap-water is influenced by many factors, which is a typical nonlinear dynamic system. Both neural networks and Volterra series are widely used in nonlinear dynamic system. This paper discusses the relations between Volterra series and BP neural network, and proposes the Volterra series-based neural network and the solution of the hight order Volterra series kernel. In this paper, the ARMA model, BP neural network and Volterra series-based neural network are applied to short-term forecast a community tap-water flows. According to the results of the comparison, it shows that the Volterra series-based neural network is better than other methods.
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