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

Lin Wei,Yongqing Wu,Hua Fu,Yuping Yin

doi:10.1155/2018/9013839

Lin Wei, Yongqing Wu + Show 2 more

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https://doi.org/10.1155/2018/9013839

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Abstract

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.

Highlights

In the daily management of mine safety, an effective method of prevention and control of mine gas disasters is the scientific analysis of the gas emission data provided by the monitoring system [1]
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
Most recent work mainly focuses on different methods for improving the prediction performance of the absolute gas emission quantity, such as Grey theory [3], principal component regression analysis method [4], partial least squares support vector machine [5], virtual state variables and Kalman filter [6], BP neural network [7], and RBF neural network [8]

Summary

Introduction

In the daily management of mine safety, an effective method of prevention and control of mine gas disasters is the scientific analysis of the gas emission data provided by the monitoring system [1]. It is expected that recurrent neural network possesses better performance than feedforward neural network (such as BP and RBF) in modeling and predicting gas emission quantity. A common drawback of the above gas emission prediction models based on classic Elman neural network is that the recursive part of the hidden layer cannot be adjusted because it is fixed. This drawback affects the nonlinear approximation ability of classic Elman neural network

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Results

Conclusion