A Green-IKE Inference System Based on Grey Neural Network Model for Humanized Sustainable Feeling Assessment about Products

Abstract

Due to extraordinary concerns about the issue of environmental protection from time to time, so far, sustainable development draws much attention. In developing sustainable products, the studies on methodologies of how to precisely grasp the sustainable feeling about products and translate it into desired constructed elements are scarce. This study aims to propose a novel sustainable feeling assessment system about products, called green-initiative Kansei engineering (GIKE). The Kansei engineering scheme is a distinguished customer-oriented technology for dealing with peoples’ affection about concerning matters. In this study, we extend Kansei engineering to initiatively include the designated sustainable image other than statistically obtained high-ranking images. Then, through survey and analysis of concerning matters, we precisely build a GIKE inference system via the grey-model-based backpropagation neural network scheme, in which it provides a precise relationship between affective (including sustainable) images of products and their constructive elements. A computer mouse is selected as the target in experiments to verify the proposed methodology, and the result is satisfying. Through our study, we may know the way to acquire a human’s sustainable feeling about concerning matters. And, most importantly, the proposed GIKE methodology firstly and innovatively expands the application filed of Kansei engineering to the field of sustainability evaluation and translation for concerning matters.