Neural networks kansei expert system for wrist watch design

In Kansei engineering, several multivariate analyses are used for analyzing human feelings and building rules. Principal component analysis is used for analyzing semantic structure. Although this method is reliable, they are time and computing resource consuming and require user's statistical expertise. In this paper, we introduce 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 ART 1.5, a variant of the Adaptive Resonance Theory neural network. It is used as a stable non-hierarchical cluster analyzer and feature extractor, even in a small sample size condition. PCAnet is based on Sanger (1989) , it performs principal component analysis. These networks enable quick and automatic rule building in Kansei engineering expert systems.