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Application of Evolutionary Neural Networks on Optimization Design of Mobile Phone Based on User's Emotional Needs
Author(s) -
Guo Fu,
Qu QingXing,
Chen Peng,
Ding Yi,
Liu Wei Lin
Publication year - 2015
Publication title -
human factors and ergonomics in manufacturing and service industries
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.408
H-Index - 39
eISSN - 1520-6564
pISSN - 1090-8471
DOI - 10.1002/hfm.20628
Subject(s) - kansei engineering , mobile phone , artificial neural network , computer science , kansei , pairwise comparison , genetic algorithm , multidimensional scaling , backpropagation , software , product design , artificial intelligence , data mining , human–computer interaction , machine learning , product (mathematics) , mathematics , telecommunications , geometry , programming language
Taking users’ emotional needs into consideration, this research aims to propose a new method to present product design features exactly and completely. On the basis of genetic algorithm integrated with back‐propagation (BP) neural networks, taking the mobile phone as research object, an optimization design algorithm was finally designed. First, the continuous and discrete design variables that describe mobile phones were screened with methods of dimensions, coordinate label, and morphological analysis. Forty three‐dimensional (3D) mobile phone models were designed by using 3D design software PROE. Accordingly, 12 representative mobile phones were selected through multidimensional scaling analysis and cluster analysis. Fourteen pairwise Kansei image words were obtained by collecting, screening, surveys, and statistical analysis method. Second, a BP neural networks model between design variables and user preference along with Kansei image words was established and verified with questionnaire survey data. Finally, the optimization design model for mobile phones was established considering design requirements and users’ emotional needs. A genetic algorithm integrated with BP neural networks was used to optimize mobile phone design. The results show that the optimization scheme is superior to others, and this paper will provide design suggestion for mobile phone designers.