Abstract
Kansei engineering is a technology for translating human feelings into product design. Several multivariate analyses are conventional techniques for analyzing human feelings and building translation rules. Although these methods are reliable, they are nevertheless time and resource consuming, and require statistical expertise for users. In this paper, we propose an automatic semantic structure analyzer and Kansei expert systems builder using self-organizing neural networks, ART1.5-SSS and PCAnet. 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. PCAnet performs principal component analysis. The networks enables quick and automatic rule building in Kansei engineering expert systems.
