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Evaluating aircraft cockpit emotion through a neural network approach

Published online by Cambridge University Press:  05 November 2020

Yanhao Chen Open the ORCID record for Yanhao Chen [Opens in a new window] ,
Suihuai Yu ,
Jianjie Chu ,
Dengkai Chen  and
Mingjiu Yu
Yanhao Chen*
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Suihuai Yu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Jianjie Chu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Dengkai Chen*
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Mingjiu Yu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
*
Author for correspondence: Yanhao Chen, E-mail: [email protected]
Author for correspondence: Yanhao Chen, E-mail: [email protected]
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Abstract

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.

Keywords

Aircraft cockpit interioremotional evaluation modelKansei engineeringneural network prediction
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 1 , February 2021 , pp. 81 - 98
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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