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

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.

关键词： Elman neural network time series Progressing Cavity Pump (PCP) speed prediction Short term

作者: Lv Xiao-ren Luo Xuan Wang Shi-jie Nie Rui

作者单位: Shenyang University of Technology, Shenyang 110870, China

会议类型: 国际会议

会议名称: 2012 International Conference on Advanced Materials Design and Machanics(2012先进材料设计与机械学国际会议 ICAMDM 2012)

会议地点: 厦门

会议语种:英文

页码: 749-753

在线出版日期: 2012-06-05（万方平台首次上网日期，不代表论文的发表时间）