DocumentCode :
2325309
Title :
Volterra Series-based Neural Network and its Application in Tap-water Flow Forcast
Author :
Kun, Chen ; Lixiong, Li
Author_Institution :
Sch. of Mechatron. Eng. & Autom., Shanghai Univ., Shanghai, China
Volume :
1
fYear :
2011
fDate :
28-30 Oct. 2011
Firstpage :
97
Lastpage :
100
Abstract :
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.
Keywords :
Volterra series; autoregressive moving average processes; backpropagation; forecasting theory; neural nets; water supply; ARMA model; BP neural network; Volterra series kernel; Volterra series-based neural network; community tap-water system; nonlinear dynamic system; short-term forecast; tap-water flow forcast; Analytical models; Biological neural networks; Communities; Equations; Mathematical model; Predictive models; Time series analysis; BP neural network; Volterra series; time series; water flow;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4577-1085-8
Type :
conf
DOI :
10.1109/ISCID.2011.33
Filename :
6079576
Link To Document :
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