Kansei Image Evaluation of Teacup based on GA-ELM

摘要：

　　In order to improve the design efficiency of cultural product , the Genetic Algorithm-Extreme Learning Machine (GA-ELM) network model is used to evaluate the Kansei image of cultural product. Firstly, the design variables of teacup are analyzed, and then 27 kinds of 3D experimental samples are constructed based on the orthogonal experimental results. Semantic Difference Method (SD method) is used to quantify the samples Kansei image score. The Kansei image evaluation model of GA-ELM cultural products is trained with the design variables of teacup as input parameter and the Kansei image score as output parameter. The reliability of the model is demonstrated by case design. The results show that using GA-ELM model to predict the Kansei evaluation of cultural products has higher feasibility and reliability, can effectively improve the emotional design efficiency of cultural products, and provide direction guidance for design practice.

关键词： Cultural products Teacup GA-ELM Kansei image Design evaluation

作者: Qi Jiang Li Peng

作者单位: Guizhou University, School of Mechanical Engineering,Guiyang, China

会议类型: 国际会议

会议名称: 2018 3rd International Conference on Mechatronics and Information Technology (ICMIT 2018) (2018第三届机电与信息技术国际会议)

会议地点: 成都

会议语种:英文

页码: 1-8

在线出版日期: 2018-10-30（万方平台首次上网日期，不代表论文的发表时间）