An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness
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Affective product design aims at incorporating customers’ affective needs into design variables of a new product so as to optimise customers’ affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximise customers’ affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers’ affective responses to the design variables of a new product. Customer survey data are usually fuzzy since human feeling is usually fuzzy, and the relationship between customers’ affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, we extend the existing work on fuzzy regression by first utilising an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method.
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