# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 4 Simulation Experiments
### Compared with Other Algorithms  
Experiments comparing AGNG-NU with other classical algorithms such as SOM [25] and GCS [26] are carried out, and two background images are used, one with fewer obstacles and the other with more. The parameters of the three algorithms are set thus: the number of loop traversal is 100, the maximum number of nodes is 100, the total number of input samples is 500, and the sample points are randomly generated in the non-obstacle part. The experimental results are shown in Figure 8, from which it can be clearly seen that the topology maps of GCS and GNG are significantly better than the SOM algorithm. The topological maps generated by GCS and GNG are indistinguishable from the naked eye.  
The comparison of the two evaluation indicators of distribution and accuracy obtained by SOM, GCS, and AGNG-NU on map 1 and map 2 is shown in Figure 9. Accuracy1 and Distribution1 are data on map 1, while the other data are on map 2. It can be seen from the comparison histograms that AGNG-NU has the best accuracy and distribution. Compared with the SOM algorithm, the accuracy is improved by 32.31%, and the distribution is improved by 47.20%; compared with the GCS algorithm, the accuracy is improved by 5.71%, and the distribution improved by 2.76%.  
SOM has two main limitations. First, the network structure and dimensionality must be determined before learning. This limits the accuracy of the resulting mapping and output. Second, the network capacity is predefined by the number of network nodes and learning parameters. This makes the network unsuitable for continuous learning or learning on non-stationary datasets. For non-uniform input environments, it is impossible to predetermine a neighborhood connectivity architecture that closely matches the data manifold structure, so SOM cannot provide a perfect topology mapping.  
Compared with the SOM algorithm, GCS has a self-organizing growth stage. The number of neurons in the GCS algorithm can be increased, and the network that can add nodes in its map space can more accurately approximate the input space.  
The GNG algorithm is also developed from the SOM and GCS algorithms. In comparison, it not only has the advantages of the GCS algorithm in the growth stage and the deletion mechanism to make up for the disadvantages of the SOM algorithm, but also uses the topological domain function and makes a great simplification in determining the calculation method of excited neurons. Featured by dynamic response deletion mechanism and adaptive adjustment mechanism of neuron parameters, the AGNG-NU proposed in this paper has had improvements on the basis of the basic GNG algorithm, in order to be more suitable for unstructured environment.  
Therefore, from the comprehensive topology maps and evaluation indicators, it can be concluded that AGNG-NU has better topology map generation performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Scene** & **Accuracy by** & **Accuracy by** \\  & **Basic GNG** & **AGNG-NU** \\ \hline
1 & 0.00652 & 0.00618 \\
2 & 0.00606 & 0.00587 \\
3 & 0.00630 & 0.00577 \\
4 & 0.00649 & 0.00607 \\
5 & 0.00601 & 0.00586 \\
6 & 0.00606 & 0.00574 \\
7 & 0.00535 & 0.00492 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment data of accuracy indicator.  
Figure 8: Comparison diagrams of SOM, GCS and AGNG-NU simulation experiments: (**a**) SOM topology on map 1; (**b**) SOM topology on map 2; (**c**) GCS topology on map 1; (**d**) GCS topology on map 2; (**e**) AGNG-NU topology on map 1; and (**f**) AGNG-NU topology on map 2.  
Figure 9: Comparison histograms of evaluation indicators by SOM, GCS, and AGNG-NU: (**a**) Comparison histograms of accuracy; (**b**) Comparison histograms of distribution.