Product Innovation Design Method Based on BP Neural Network. (11th October 2022)

• Record Type: Journal Article
• Authors: Huang, Shihui
• Other Names: Dziubinski M. Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2022(2022)
• Publication Date: 2022-10-11
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2022/6830892
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : Innovative product design is the core problem of modern industry and the source power of enterprise development. With the continuous improvement of China's material level, product innovation design has changed from functional needs to emotional needs, and the product design method centered on users' emotional needs has attracted much attention. With the emergence of new technologies such as artificial intelligence and big data, the automation and intelligence of advanced algorithms and innovative design methods for online product decision driven by multimodal multimedia data in e-commerce have become inevitable trends in the field of product design, and it is a big challenge to improve the innovative design ability of products by using information technology. In order to solve this problem, based on the research of BP neural network technology, this paper puts forward an innovative product design method based on image-Kempi method. The color features of images are reconstructed and integrated, and then transferred to product modeling, so as to generate new products with both content image modeling features and style image color features, which brings visual inspiration to users. The results show that KENPI method combines the theories of Kansei engineering, convolutional neural network, and neural style transfer, and establishes a mapping model between product modeling elements and product semantics. Convolutional neural network and neural style transfer model are used to extract, reconstruct, and integrate the color features of style diagrams, and transfer them to product modeling to design new product images. By evaluating the image quality and comparing the product semantics before and after the migration, the validity and feasibility of the migration are verified. Based on BP neural network, a nonlinear mapping model between product attribute space and product semantic space is constructed, and the generalization ability of the model is evaluated, which verifies the feasibility and effectiveness of this method. The research results have important theoretical guiding significance for improving the innovative design ability of enterprises, enhancing product competitiveness, and customer satisfaction.