Title :
Study of modern industria product optimization design based on image
Author_Institution :
Coll. of Inf. Sci. & Eng, Henan Univ. of Technol., Zhengzhou, China
Abstract :
To design the acceptable products that meet consumer emotional demands is very important, and a novel method is proposed combiningg neural network with genetic algorithm(GA). In this paper, a back propagation neural network(BPNN) is applied to map the relationships between product design elements and customer kansei image evaluation. And then, GA is employed to search for the optimal product form which satisfies customer requirement by using the trained neural network. In the end, the framework of product image form optimization design system using Virtual Reality Modeling Language(VRML) is analyzed. To test the method, an example of kettle design is used to study, the results show that this method is valid and feasible.
Keywords :
backpropagation; genetic algorithms; neural nets; product design; virtual reality languages; VRML; back propagation neural network; consumer emotional demand; customer kansei image evaluation; customer requirement; genetic algorithm; kettle design; modern industrial product optimization design; optimal product; product image; trained neural network; virtual reality modeling language; Biological cells; Genetic algorithms; Image color analysis; Object oriented modeling; Optimization; Product design; Solid modeling; back propagation neural network; genetic algorithme; optimization design; product design;
Conference_Titel :
Multimedia Technology (ICMT), 2011 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-61284-771-9
DOI :
10.1109/ICMT.2011.6002317