Open AccessA HYBRID KANSEI ENGINEERING SYSTEM USING THE SELF-ORGANIZING MAP NEURAL NETWORK
Author(s) -
Chee Siong Teh,
Chee Peng Lim
Publication year - 2016
Publication title -
journal of it in asia
Language(s) - English
Resource type - Journals
ISSN - 1823-5042
DOI - 10.33736/jita.53.2007
Subject(s) - kansei engineering , kansei , process (computing) , computer science , artificial neural network , artificial intelligence , feeling , associative property , product (mathematics) , self organizing map , machine learning , engineering design process , intelligent decision support system , human–computer interaction , engineering , mathematics , psychology , mechanical engineering , social psychology , geometry , pure mathematics , operating system
Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and the relationships with the product design decisions have been introduced in KE systems, many of the approaches are not able to incorporate human subjective feelings and preferences into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abilities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate the effectiveness of the proposed approach in developing an intelligent KE system which is able to combine human feelings and preferences into its decision making process.