2009 IITA International Conference on Control, Automation and Systems Engineering
Predicting the Amount of Gas Emitted Based on Wavelet Neural Network
Xue Pengqian
Zhang Xiaoyu
Department of Electronic Information North China Institute of Science &Technology Beijing, China e-mail:
[email protected]
Department of Electronic Information North China Institute of Science &Technology Beijing, China e-mail:
[email protected]
Abstract—Precisely predicting the amount of gas emitted from the mine, which is a matter of nonlinear model related to many factors such as nature characters and mining technology, is of great importance in the design of mine and production safety. As back-propagation neural networks (BPNN) have the shortcomings of slow convergence and being prone to fall into local optimums, a new method of wavelet neutral network which can make full use of its time-frequent characteristics and combination with the self-study ability of neutral networks is presented to form a model for predicting the amount of gas emitted from the mine. Based on this model, using Matlab6.5, the simulator of wavelet and BP neural network is designed. The simulation results obtained show that the new method can achieve faster convergence and more accurate prediction compared with that of BPNN. Keywords-wavelet neural quantity ;nonlinear ;predicting
I.
network;
gas
emission
INTRODUCTION
Coal mine gas is the main insecurity factor in production process. According to statistics, more than 95% coal mine accident is caused by gas. Accurate predicting the amount of gas emitted has great significance for prevention of gas outburst, gas accumulation and coal mine gas explosion accidents to ensure the security of the coal mines. In addition, the ventilation design and development of mine safety technology are based on the gas emission of coal mine. Accuracy prediction results has direct influence on the mine's economic and technological guideline. Therefore, the prediction of gas emission is a key technology problem and domestic and foreign scholars have been concerned how to predict. However, gas emission is affected by many factors, such as geological structure, coal thickness, coal structure, burial depth, as well as natural factors, such as mining technology. And it is a dynamic variable process. In addition, because of the various factors influencing each other, it is a very complex system, and many factors forming part of the system are changing randomly. Therefore, the gas emission is difficult to set up a direct relationship with the impact of the factors. It is difficult to predict gas emission. At present, domestic gas emission prediction method can be generally classified as mining statistical method based mathematical statistics, sub-source method, analogy, etc. These methods are the use of regression and numerical analysis way to set up gas emission factors and their impact 978-0-7695-3728-3/09 $25.00 © 2009 IEEE DOI 10.1109/CASE.2009.83
on the relationship between the linear prediction, but because of the complexity of the calculation process and a large quantity of data processing, accuracy is not high and not reflect on the impact of gas emission in the dynamics of fuzzy relations. Moveover, these prediction methods have not take gas emission as dynamic non-linear complex systems. Some scholars believe that gas emission in coal mine is a typical gray system [1]. Gray prediction theory has the accuracy of prediction, but has difficult to accurate modele and solves at many factors circumstances. In view of the relationship between gas emission and effect factors is compex nonlinear. Neural network technique is a valuable pattern recognition method in theory and application. It is widely used in engineering application especially to deal those issues concerned in non-linear or complicated systems. Therefore, the neural network set up gas emission prediction is an effective method [2], but the neural network study convergence process for too long, easy to fall into local minimum, less robust. In response to these circumstances, the paper presents a new prediction method based on wavelet neural networks, which can effectively avoide getting into partial minimum values and conquers the inherent flaw of conditional BP net,and improves training speed as well as . II.
WAVELET NEURAL NETWORK
Wavelet neural network [3-4] is a new kind of neural networks, which is formed by combining the wavelet theory and artificial neural network. It not only can take full advantage of wavelet transform the nature of the local ,but also combination of neural network of self-learning ability, which has strong approximation and fault tolerance.so it has faster convergence speed and better forecasting results Structure of wavelet neural network The wavelet neural network is a coalition of wavelet analyses and feed-forward neural network. The node functions of the hidden layer of the single hidden layer neural net-work are replaced by wavelet functions. The weight s for the connecting links and the thresholds of the hidden layer are replaced by the scale parameters and the move parameters of the wavelet functions. The wavelet transformation has time-frequency domain local p roperty and multi-scaling p roperty. It has particular advantage in signal analysis; the wavelet neural network A.
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based on wavelet analysis theory is superior to the conventional neural network in solving complicated nonlinear p roblems.The wavelet neural network structure is as shown in Fig.1.
ω1 ω2
ωL
The error function used for training the WNNC is defined as Eq. (3). The network parameters amend are as follows:
ϖ j (l + 1) = ϖ j (l ) − ηϖ a j (l + 1) = a j (l ) − ηa
∂E + aa Δa j (l ) ∂a j
(6)
b j (l + 1) = b j (l ) − ηb
∂E + ab Δb j (l ) ∂ϖ j
(7)
Where Fig.1 the Structure of wavelet neural network
Suppose functionψ (t ) ∈ L ( R ) , the function is also called as the mother wavelet that satisfies the admissibility condition, i. e. 2
2
ψ (ω ) Cψ = ∫ dω < ∞ ω −∞ where ψ (ω ) is the Fourier transform +∞
(1)
ofψ (t ) . Then, the corresponding wavelet function family by scaling and translating is defined for
{ψ } ψ a ,b
( a, b ∈ R
a ,b
(t ) = a
−1
2
ψ(
and a ≠ 0)
t −b ) a
(2)
Where a and b are respectively the scale and translation parameters. Fig. 1 shows a 3-layer structure of network that is composed of an input layer, a hidden layer and an out put layer. The neuron number of the input, hidden and output layer is 6, 10, and 1 respectively. Every neuron’s transfer function in the hidden layer is defined as the wavelet function family ψ a j ,b j (t ) ; the neuron of output layer is
∂E + aϖ Δϖ j (l ) (5) ∂ϖ j
ηϖ ,ηa ,ηb
is
learning
rate
of
ϖ , a, b
,
respectively. aϖ , aa , ab are momentum factor. The steps of base flowchart of wavelet neural network are showed in Fig. 2: fist of all, the digital logs will be preprocessed. Second, the neural network will be trained with samples which are representative wells. They are log data of the representative standard wells. Then the t rained WNN can recognize the base-level cycle patterns which it “remembered”in the t raining step in the log data of the matched well. At last, the result will be checked artificially. III.
GAS EMISSION PREDICTION MODEL AND SIMULATION
A. Forecasting Model We set up the wavelet neural network prediction of gas emission mode, as shown in Fig.3, which mainly consists of three parts: sample foreclose, Wavelet Neura l Network and output. First of all, the input sample pretreatment.we make buried coal seam depth, spacing etc. six characteristic values of input sample pretreatment .So, the wavelet neural network input layer of six nodes, the implied layer 10 nodes, one output one layer node, the output for the gas emission absolute value.
linear and adds linearly wavelet family of the hidden layer. B. Wavelet Neura l Network Learn ing Algorithm For input and output sample ( xl , yl )(l = 1, 2," L) ,the parameterϖ j , a j , b j can be obtained and optimized by LMS(Least Mean Search) energy function:
1 L E = ∑ [ fl ( x) − yl ]2 2 i =1 Where ψ ( x ) is morlet wavelet,
ψ ( x) = cos(1.75 x)e
1 − x2 2
(3)
B. Wavelet Neural Network Simulation prediction s In accordance with the above principles and steps to using Matlab6.5 produced a wavelet / BP neural network simulator, Initializtion parameters are set as follows: learning rate were taken 0.5 and momentum factor were taken 0.02,E
