# A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
## 5 Conclusions  
This paper presents a CP-modeling design method (CP-KEDL) based on the LSTM neural network and KE. Compared with the traditional KE methods or neural networks, CP-KEDL regards the user's observation behaviors of CPs as a visual process, which can be obtained through eye-movement experiments, so that CPs can be decomposed into modeling-feature sets, including visual observation sequence data. The experiment results show that adding the visual sequence to neural-network modeling makes the input features more comprehensive and more consistent with the objective laws of user observation and perception, so as to obtain a more comprehensive and accurate sample-modeling feature coding. It can more effectively solve the problem of the mapping model of CP perceptual images and modeling features and generate the optimal recommended modeling feature  
Figure 13: Comparison of the training losses of DNN, CNN, and LSTM models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Structure** & **MSE** & **RMSE** \\ \hline LSTM & 0.02 & 0.14 \\ CNN & 0.23 & 0.48 \\ DNN & 0.30 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results of LSTM, CNN, and DNN models. set. With this method, designers can be assisted in meeting users' perceptual image needs more accurately and conduct the modeling design of CPs more quickly and effectively. The main contributions of the article are as follows:  
1. We argue that users' visual sequence will affect their perception and evaluation when observing CPs, and the user's observation sequence should be taken into account when establishing the mapping relationship model between the product modeling features and the perceptual images. 2. The neural network of LSTM was applied to construct a perceptual evaluation model (KE-LSTM) in order to effectively handle the timing data. It could simulate the visual sequence of CPs observed by human eyes, effectively process the modeling information of CPs with temporal characteristics, and improve the robustness of the model. Moreover, KE-LSTM has a higher model accuracy than DNN and CNN. 3. To deconstruct the modeling features of CPs, we propose an improved MA method based on the temporal-association function. It encodes the representative samples into a modeling feature set including visual sequence data and facilitate the input of the LSTM neural network to mine its timing information and improve the accuracy of the model. The modeling design of CPs is always restricted by its functions, so the establishment of perceptual evaluation models can only be applied to a variable range of modeling features. Future research can be the construction of a mapping model between function modeling and perceptual evaluation based on the actual needs of customers. In the meanwhile, as the accuracy of machine learning always depends on the size of samples to be evaluated, the participants have to spend great energy and time scanning and evaluating the large number of samples, which limit inviting more experts to participate in the evaluation experiment. The authors guess that the evaluation data may be more accurate and appropriate if we could invite more experts as our participants. This will be our future direction and motivation. Conceptualization, J-J.D. and P-S.L. ; methodology, J-J.D. ; software, P-S.L. ; validation, F-A.S., Q.L. and P-S.L. ; formal analysis, P-S.L. ; investigation, F-A.S.; resources, L.-M.Z. ; data curation, J-J.D. ; writing--original draft preparation, J-J.D. and Q.L. ; writing--review and editing, J-J.D. ; visualization, P-S.L. ; supervision, J-J.D. ; project administration, J-J.D. ; funding acquisition, J-J.D. All authors have read and agreed to the published version of the manuscript. This research was funded by Chinese Ministry of Education's Collaborative Education Project of Production and Education, grant number 202102071001. Informed Consent Statement: Informed consent was obtained from all participants involved in the study.