Mathematical Problems in Engineering

Volume 2018 (2018), Article ID 9013839, 10 pages

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

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

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

Correspondence should be addressed to Lin Wei

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.

#### 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.

#### 1. 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]. The gas is one of the most important factors threatening the safety production of mine [2]. 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].

In recent years, intelligent computing methods have been rapidly developed in dynamic system identification [9, 10], time series prediction [11, 12], and other fields. In fact, there are many factors influencing the absolute gas emission quantity, such as coal seam gas content, burying depth, and coal seam thickness [13, 14]. That means the gas emission prediction model is a multidimensional complex dynamic system, and it is difficult to accurately predict gas emission quantity, since recurrent neural network is a highly nonlinear dynamical system that exhibits complex behaviors and good ability of processing dynamic information [15]. As is well known, the recurrent neural network has wide applications in various areas [16, 17]. 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. In particular, Elman neural network (ENN) has been proved successful in gas emission prediction [18, 19]. Some works on improving the performance of gas emission prediction using ENN can be found in, for example, [20–22]. However, 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.

From the above observation, this paper proposes a novel strategy of adding correction factors in recursive part of ENN, resulting in a new model called recursive modified Elman neural network (RMENN). The stability and convergence of RMENN model are theoretically proved, and some meaningful results are obtained in this paper. In practice, through the analysis of the main factors affecting coal gas emission, this paper puts forward the gas emission prediction model based on recursive modified Elman neural network.

The rest of this paper is organized as follows. The establishment of RMENN model is described in Section 2. The learning algorithms of RMENN model are described in Section 3. The performance analysis and flowchart of RMENN model are described in Section 4. Experiment analysis results on the gas emission prediction are presented in Section 5. Finally, the paper is concluded in Section 6.

#### 2. Establishment of RMENN Model

As discussed in Section 1, we aim to propose a specific architecture to overcome the aforementioned drawback of a fixed structure and improve the nonlinear approximation ability of ENN. In Figure 1, this paper adds correction factors in the context layer and the output layer to adjust the values of the recursive parts. Let and denote the network input and output vectors at the discrete time , respectively. Let denote weight matrices of context-hidden, input-hidden, hidden-output, and output-hidden, respectively. Let and denote the output vectors of the context layer and the hidden layer at the discrete time , respectively. Let be the correction part of the hidden context layer. Let be the correction part of the output context layer. and are the activation functions, respectively. In a general way, is the sigmoid function and is the linear function. Let be the output vector of the output context layer at the discrete time .