Citation: INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS Volume: 19 Issue: 2 Pages: 93-104
Abstract: Kansei engineering is a technology for translating human feelings into product design. Several multivariate analyses are used for analyzing human feelings and building rules. Although these methods are reliable, they require large computing resources. It is difficult for general users to deal with many variables because of small personal computers, and the need for the user to be an expert on statistics. This paper presents 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. It is used as a stable non-hierarchical classifier and a feature extractor, in a small sample size condition. PCAnet performs principal component analysis based on generalized Hebbian algorithm by Sanger (1989). These networks enable quick and automatic rule building in Kansei engineering expert systems. AKSYONN4 system is the automatic builder for Kansei engineering expert systems because it uses self-organizing neural networks. The system enables real-world applications of Kansei engineering in product development.