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Kansei Image Evaluation of Teacup based on GA-ELM

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DOI: 10.23977/icmit.2018.038


Qi Jiang, Li Peng

Corresponding Author

Qi Jiang


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.


Cultural products, Teacup, GA-ELM, Kansei image, Design evaluation

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