# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 4 Simulation Experiments
### Compared with the Basic GNG Algorithm  
In order to verify the effectiveness of the proposed AGNG-NU algorithm and its advantages in the non-uniform input environment, a large number of simulation experiments has been carried out on the Matlab platform with the construction of the tunnel section as the background, which was compared with the basic GNG algorithm. The initial parameters of the simulation are set as follows: the number of loop traversals \(Num\ of\ Epochs=100\), the total number of sample inputs \(Num\ of\ Samples=1000\), the maximum age value \(max\_age=140\), the maximum number of nodes \(max\_nodes=200\), and the weight adjustment parameters \(eb=0.085,en=0.0045\). The input signal \(\xi(\xi_{x},\xi_{y},\xi_{e})\) is the three parameters obtained by the environment identification model of tunnel section, namely \(\xi_{x}\) is the ordinate of the cutting head in the section, \(\xi_{y}\) is the abscissa of the cutting head in the section, and \(\xi_{e}\) is the environment normalization parameter. In the simulation experiment, the special situation of the existence of obstacles such as dirt band in the environment is considered to form a non-uniform input signal environment. The experimental scene settings are described as follows:As shown in Figure 6, the obstacles are set on both sides of the vertical symmetry axis of the topology map distribution and are symmetrical about the vertical symmetry axis. 2. The contrast area of the graphic distribution is the left, middle, and right parts, and the two separated parts remain the same without modification. 3. Table 2 is the experimental environment scale setting table for seven input points with different distribution ratios. By changing the distribution of input sample points, the simulation results of taking the average value of 10 experiments are shown in Figure 7, which are seven groups of simulation experiment results, among which (a), (c), (e), (g), (i), (k) and (m) are the topology diagrams obtained by the basic GNG algorithm under the experimental setting conditions, and (b), (d), (f), (h), (j), (l) and (n) are the AGNG-NU simulation diagrams. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Experimental Scene**} & \multicolumn{1}{c}{**The Ratio of the Number of Input Points on Left,**} \\ \multicolumn{1}{c}{**Middle, and Right Part**} \\ \hline
1 & 1:1:1 \\
2 & 1:3:5 \\
3 & 5:3:1 \\
4 & 2:2:5 \\
5 & 2:5:2 \\
6 & 1:1:7 \\
7 & 16:1:1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment scale setting table. Figure 6: Comparison chart of input nodes area distribution. Figure 7: _Cont._  
Figure 7: _Cont_. It can be seen from the simulation diagrams that in experiment 1, where the input sample points are uniformly distributed, the connecting edges of the GNG algorithm is not as good as that of the AGNG-NU algorithm, but the difference is not obvious enough. However, in experiments 2 to 7, where the input sample points are non-uniformly distributed, the GNG experiments simulation results show more broken lines and large blank areas, while the results of AGNG-NU show that in the area with dense input signals, the more neuron nodes are described, the more abundant the connecting edges between nodes, and the clearer the environmental information expression. For the evaluation indicators, the distribution data obtained by the basic GNG algorithm and AGNG-NU are shown in Table 3, and the accuracy data are shown in Table 4.  
It can be seen from Table 3 that, under all experimental settings, the distribution indicator obtained by AGNG-NU is better, and compared with the GNG algorithm, the distribution indicator can be increased by up to 15%. Table 4 shows that the accuracy indicator obtained by AGNG-NU is better, which can be improved by up to 8%. From the comprehensive topological diagrams and evaluation indicators, it can be concluded that the map constructed by AGNG-NU achieves better results than GNG algorithm.