Short-term Prediction on the Time Series of PCP Speed Based on Elman Neural Network

被引：0

作者：

Lv Xiao-ren ^{[1
]}

Luo Xuan ^{[1
]}

Wang Shi-jie ^{[1
]}

Nie Rui ^{[1
]}

机构：

[1] Shenyang Univ Technol, Shenyang 110870, Peoples R China

来源：

ADVANCED MATERIALS DESIGN AND MECHANICS | 2012年 / 569卷

关键词：

Elman neural network; time series; Progressing Cavity Pump (PCP) speed prediction; Short term;

D O I：

10.4028/www.scientific.net/AMR.569.749

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.

引用

页码：749 / 753

页数：5