AN AUTOMATIC BUILDER FOR A KANSEI ENGINEERING EXPERT-SYSTEM USING SELF-ORGANIZING NEURAL NETWORKS

Kansei Engineering is a technology for translating human feelings into a product design. Linear multiple regression analysis is used as a tool to analyze the feelings-design relations and building rules for Kansei Engineering expert systems. Although the method is reliable, it is nevertheless, time and resource consuming and requires statistical expertise in relation to its mathematical constraints. In this paper, we introduce an automatic builder of Kansei expert systems using a self-organizing neural network ART1.5-SSS. ART1.5-SSS is our modified version of ART1.5, a variant of the Adaptive Resonance Theory neural network. Improvement on learning rule makes ART1.5-SSS a stable non-hierarchical cluster analyzer and feature extractor, even in a small sample size condition. The network enables quick, automatic rule building in Kansei Engineering expert systems. The categorization and feature selection performance of our new learning rule is compared to multivariate analyses and to the original ART1.5.