Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network
Joint Authors
Wei, Lin
Wu, Yongqing
Fu, Hua
Yin, Yuping
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wei, Lin& Wu, Yongqing& Fu, Hua& Yin, Yuping. 2018. Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1209520
Modern Language Association (MLA)
Wei, Lin…[et al.]. Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network. Mathematical Problems in Engineering No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1209520
American Medical Association (AMA)
Wei, Lin& Wu, Yongqing& Fu, Hua& Yin, Yuping. Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1209520
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209520