# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 2 Environment Identification Method and Model Establishment of Tunnel Section
### Load Identification Method of Cutting Head Based on Training of Measured Data  
During the cutting process of the roadheader, the load on the cutting head is affected by a variety of factors. The changes of these factors are often accompanied by changes in multiple working parameters. Therefore, it is impossible to establish an accurate mathematical model between these working parameters and the cutting head load.  
The cutting motor voltage \(U\), cutting motor current \(I\), driving cylinder pressure \(P\), and cutting arm vibration acceleration \(Acc\) are used as four neurons in the input layer, the output layer of the neuron network is set to have one neuron, and the corresponding output is the hardness of coal rock \(f\). A BP neural network has the function of realizing any complex nonlinear mapping. A three-layer BP neural network can realize arbitrary nonlinear function approximation and can complete multi-sensor information fusion.  
In the designed BP neural network prediction controller, the selected training function is Levenberg-Marquardt, the training algorithm is trainlm, the activation function of the hidden layer is the logarithmic sigmoid activation function logsig, and the activation function of the output layer is the linear activation function purelin. The target error of  
Figure 1: Schematic diagram of the unstructured section environment recognition model.  
training is set as \(1\times 10^{-3}\), which is far higher than the requirement of cutting control accuracy in engineering [19], and the maximum number of iterations is set to 1000 times.  
In order to determine the number of neurons in the hidden layer of the network, 500 groups of sample data are selected to train the neural network with different numbers of neurons in the hidden layer. The results of the training errors are shown in Figure 2.  
It can be seen from Figure 2 that the minimum value of reaching the set target error occurs when the number of neurons in the hidden layer is 10. Therefore, the number of neurons in the hidden layer of the BP neural network is set to 10 and the training of the BP neural network is started. Then, 100 groups of samples are taken to test the network, and the training and testing results of the BP neural network are shown in Figure 3.  
It can be seen from Figure 3 that the training and testing results of the BP neural network can achieve the set target, and the error decreases faster and the number of iterations is lower.  
Through the BP neural network method trained by the measured data, the fusion criterion of the underground multi-sensor information and the normalized parameter of coal rock hardness is obtained, as shown in Table 1 below.  
According to Table 1, it can be determined that different coal rock hardness corresponds to the range of 0 to 1, and the correspondence between coal rock hardness of tunnel section and coal rock hardness normalized parameter of multi-sensor information is established. It enables the identification of the tunnel section environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cutting Condition** & **Sofer Media and Coal** & **Coal-Rock Inclusions** & **Full Rock Roof** \\ \hline Normalization parameter of & & \\ coal and rock hardness & 0–0.377 & 0.377–0.852 & 0.852–1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The normalized parameter of coal rock hardness in different cutting conditions.  
Figure 3: Diagram of BP neural network training and testing results.  
Figure 2: Training errors corresponding to different numbers of neurons in the hidden layer.