文献类型：期刊文献

英文题名：On the Prediction of Product Aesthetic Evaluation Based on Hesitant-Fuzzy Cognition and Neural Network

作者：Wu, Xinying[1];Yang, Minggang[1];Su, Zishun[1,2];Zhang, Xinxin[3]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai, Peoples R China;[2]Shanghai Business Sch, 2271 West Zhongshan Rd, Shanghai, Peoples R China;[3]Hebei Univ Technol, Sch Architecture & Art Design, Tianjin, Peoples R China

年份：2022

卷号：2022

外文期刊名：COMPLEXITY

收录：;EI(收录号：20222812339752);WOS:【SCI-EXPANDED(收录号:WOS:000873224200002)】；

基金：AcknowledgmentsThis work was supported by Shanghai Philosophy and Social Fund Project (Grant no. 2019EWY010).

语种：英文

外文关键词：Complex networks - Fuzzy inference - Fuzzy neural networks - Product design

摘要：Product market competitiveness is positively influenced by the aesthetic value of product form, which is closely related to product complexity. By measuring the cognitive complexity of the product, this research establishes the relationship between the complexity and aesthetics of the product using an artificial neural network. Hence the prediction of product beauty is achieved, which guides design decisions. In this article, the complexity of product form is first measured through a combination of hesitant-fuzzy theory and information axiom. Afterward, the result is weighted by exponential entropy and dimensionally compressed. This method makes data more suitable for the prediction with small samples, obtaining an accuracy improvement of up to 40% compared with traditional approaches. Finally, the importance order of the design elements which affect morphological complexity is acquired. Results show that three of the six complexity features (element number, object intelligence, and object detail) are more significant, impacting the aesthetic feeling of product form. The method increases the attractiveness of products to customers, providing valuable design support for enterprises and designers in the early days when a new product is designed, and reducing research and development risks.