A coal and gas outburst prediction method based on rbf neural network
A gas outburst and neural network technology, applied in the field of coal mine safety production, can solve the problems of difficulty in determining the optimal parameters of the RBF neural network, slow convergence of the BP neural network, and falling into local minimum values.
Active Publication Date: 2020-05-22
HEFEI UNIV OF TECH
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For example, the BP neural network model is used to predict coal and gas outbursts. However, the disadvantages of the BP neural network are that the convergence speed is slow, the training time is long, and it is easy to fall into a local minimum.
There have been some works using RBF neural network to predict coal and gas outburst problems, but because the optimal parameters of RBF neural network are difficult to determine, and the data of coal and gas outburst in different mines in different regions are different, the optimal parameters will also vary. different
[0006] Therefore, it is difficult to determine the optimal parameters of the RBF neural network, and the differences in the coal and gas outburst data of different mines in different regions lead to different optimal parameters. An adaptive RBF neural network model with optimal parameters is needed to quickly and Accurately predict coal and gas outburst
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[0056] refer to figure 1 , a coal and gas outburst prediction method based on RBF neural network, proceed as follows:
[0057] Step 1: Obtain a set of training samples of coal and gas outburst, the training samples are made up of feature data X={x 1 ,x 2 ,···,x i ,···,x N} and classification label data Y={y 1 ,y 2 ,···,y i ,···,y N}, where N represents the number of training samples, x i Represents the i-th feature data in the training sample, and has: x i ={x i1 ,x i2 ,···,x iz ,···,x im},x iz Indicates the z-th eigenvalue of the i-th feature data in the training sample, m indicates the dimension of the feature data X; y i Represents the i-th feature data x in the training sample i Corresponding classification labels, and have: y i ={c l |l=1,2,...,C}, C represents the number of classification labels, c l Indicates the l-th classification label, i∈[1,N], z∈[1,m];
[0058]In this embodiment, taking the coal and gas outburst training sample data in Table 1 as...
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The invention discloses a coal and gas outburst prediction method based on RBF neural network, comprising: 1. performing dimension reduction on characteristic data of coal and gas outburst, and normalizing the reduced characteristic data, Obtain normalized feature data; 2. Use the K-means algorithm to cluster the normalized feature data and calculate the center of the radial basis function; 3. Train the RBF neural network on the normalized feature data and introduce adaptive The optimal expansion factor and optimal weight when the differential evolution algorithm determines the number of neurons in the hidden layer; 4. Increase the number of neurons in the hidden layer and repeat steps 2-3 to obtain the global optimal parameters of the RBF neural network, thereby Determine the prediction model of the RBF neural network; 5. Use the RBF neural network prediction model to predict the test data. The invention can solve the problem of optimal parameter self-adaptation of RBF neural network, thereby predicting coal and gas outburst quickly and accurately.
Description
technical field [0001] The invention relates to the field of coal mine safety production, in particular to a coal and gas outburst prediction method based on an RBF neural network. Background technique [0002] Coal is the main energy source and important raw material in the development of my country's national economy. However, the safety situation of my country's coal production is still very severe. Mine gas, coal dust, fire, flood and roof accidents are the five natural disasters in coal mines, among which gas is the number one "killer" of coal mines, and coal and gas outburst are the most frequently occurring gas disaster accidents and the number of people injured is large One of the typical dynamic disasters. Therefore, it is of great practical significance to quickly and accurately predict coal and gas outburst, which can not only improve the safety of coal mine production, but also generate huge economic and social benefits. [0003] Traditional coal and gas outbur...
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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/02G06N3/00G06N3/04
CPCG06N3/006G06Q10/04G06Q10/0635G06Q50/02G06N3/048
Inventor吕俊伟胡学钢李培培邵玉涵廖建兴
OwnerHEFEI UNIV OF TECH