# An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition
## 4 Experiment and Result Analysis
### Conclusions and Prospects  
This study aimed to improve the baseline KIII model. We delved into the trade-off between the accuracy and time expenditure of solving differential equations in the KIII model. The results showed that the Euler method consumes minimal time and achieves decent recognition performance. To address the KIII model's weakness in recognizing disordered signals, we introduced image enhancement and feature extraction methods in the feature extraction stage, resulting in the extraction of richer features. We validated their significant contribution to the model's recognition performance in experiments.  
In the model decision stage, we compared five measurement algorithms, and the results indicated that the Spearman rank correlation coefficient outperformed other metrics. Finally, we applied the improved KIII model to a new application domain--traffic sign recognition. On the TSRD dataset, the improved KIII model achieved a recognition rate of 95.18%, which was comparable to other classical models, demonstrating the feasibility of the improvement.  
In future research, we will focus on the following aspects:  
* From a performance perspective, we will draw inspiration from deep learning concepts. For instance, we will adjust the connection methods between neurons in the model from existing connections to local or random connections. We may also integrate classical modules from deep learning into the KIII model to enhance its performance and explore the applications of Convolutional Neural Networks and Recurrent Neural Networks as they possess distinct advantages in handling spatial and temporal data,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Iterations** & \begin{tabular}{c} **Channels** \\ **(Image Size)** \\ \end{tabular} & \
\begin{tabular}{c} **Biomimetic** \\ **Degree** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} \\ \hline AlexNet & 100 & 64 & low & 93.1\% \\ RIECNN [23] & 100 & 64 & low & 96.9\% \\ CapsNetCNN [24] & 100 & 64 & low & 97.5\% \\ Improved KIII & 1 & 64 & high & 95.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: KIII compared to other models.  
respectively. These advanced neural network architectures may play a crucial role in the analysis of olfactory data.
* From a biomimetic perspective, we will explore whether the model's structure, compared to the anatomical structure of the real olfactory neural system, can be further improved to increase its biomimicry.
* Efforts will be made to expand the application of the KIII model into additional domains, particularly in areas related to olfaction.  
Methodology, H.H.; Validation, H.H.; Resources, W.L.; Writing--original draft, H.H.; Writing--review & editing, W.L.; Supervision, L.K., F.Y. and J.Z. ([PERSON]); Project administration, J.Z. ([PERSON]); Funding acquisition, J.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province (2021 JJ30456, 2021 JJ30734), the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2022B60), the National Defense Science and Technology Key Laboratory Fund Project (2021-KJWPDL-17), the National Science Foundation of China (61972055), and the Research Foundation of Education Bureau of Hunan Province, China (20C0030).  
Not applicable.  
In this study, we used a public datasets: The TSRD, which is available at [[http://www.nlpria.ac.cn/pal/trafficdata/recognition.html/](http://www.nlpria.ac.cn/pal/trafficdata/recognition.html/)]([http://www.nlpria.ac.cn/pal/trafficdata/recognition.html/](http://www.nlpria.ac.cn/pal/trafficdata/recognition.html/)) (accessed on 18 December 2023).  
The authors declare no conflict of interest.