ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍA
Kansei Engineering (emotions) relates that consumers fee/ with features and properties that have the products through the estimation of a mathematical model, which allows for properties which are high relative to the emotions, al/owing the designer incorporate these relations to activate factors which enhance the Kansei design and give effective solutions. Traditionally the development of the mathematical model and estimation is done through a multiple regression model (QT1) and factor analysis, one of the disadvantages of the estimation of this model is that is conditioned to meet the model assumptions. This paper shows how neural networks can be applied in studies of Kansei Engineering and gives similar results, allowing for use when no statistical assumptions are met.
