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

Xiao Ren Lv; Xuan Luo; Shi Jie Wang; Rui Nie

doi:10.4028/www.scientific.net/AMR.569.749

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 569Short-Term Prediction on the Time Series of PCP...

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

Abstract:

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.