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Title: Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design
Authors: Jiang, H
Kwong, CK 
Siu, KWM 
Liu, Y
Keywords: Affective product design
Customer satisfaction
Particle swarm optimization
Rough set theory
Issue Date: 2015
Publisher: Elsevier Ltd
Source: Advanced engineering informatics, 2015 How to cite?
Journal: Advanced Engineering Informatics 
Abstract: Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the 'out of memory' error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.
ISSN: 1474-0346
DOI: 10.1016/j.aei.2015.07.005
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