TY - JOUR
T1 - Evaluating aircraft cockpit emotion through a neural network approach
AU - Chen, Yanhao
AU - Yu, Suihuai
AU - Chu, Jianjie
AU - Chen, Dengkai
AU - Yu, Mingjiu
N1 - Publisher Copyright:
Copyright © The Author(s), 2020. Published by Cambridge University Press.
PY - 2021/2
Y1 - 2021/2
N2 - Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.
AB - Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.
KW - Aircraft cockpit interior
KW - emotional evaluation model
KW - Kansei engineering
KW - neural network prediction
UR - http://www.scopus.com/inward/record.url?scp=85095755560&partnerID=8YFLogxK
U2 - 10.1017/S0890060420000475
DO - 10.1017/S0890060420000475
M3 - 文章
AN - SCOPUS:85095755560
SN - 0890-0604
VL - 35
SP - 81
EP - 98
JO - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
JF - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
IS - 1
ER -