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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 9013839, 10 pages
Research Article

Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network

1Department of Basic Education, Liaoning Technical University, Huludao, China
2School of Electrical and Control Engineering, Liaoning Technical University, Huludao, China

Correspondence should be addressed to Lin Wei; moc.qq@46166792

Received 13 October 2017; Revised 3 January 2018; Accepted 22 January 2018; Published 20 February 2018

Academic Editor: Qian Zhang

Copyright © 2018 Lin Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For the purpose of achieving more effective prediction of the absolute gas emission quantity, this paper puts forward a new model based on the hidden recurrent feedback Elman. The recursive part of classic Elman cannot be adjusted because it is fixed. To a certain extent, this drawback affects the approximation ability of the Elman, so this paper adds the correction factors in recursive part and uses the error feedback to determine the parameters. The stability of the recursive modified Elman neural network is proved in the sense of Lyapunov stability theory, and the optimal learning rate is given. With the historical data of mine actual monitoring to experiment and analysis, the results show that the recursive modified Elman neural network model can effectively predict the gas emission and improve the accuracy and efficiency of prediction compared with the classic Elman prediction model.