Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design

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@Article{Jiang:2015:AEI,
  author =       "Huimin Jiang and C. K. Kwong and K. W. M. Siu and 
                 Y. Liu",
  title =        "Rough set and PSO-based {ANFIS} approaches to modeling
                 customer satisfaction for affective product design",
  journal =      "Advanced Engineering Informatics",
  volume =       "29",
  number =       "3",
  pages =        "727--738",
  year =         "2015",
  ISSN =         "1474-0346",
  DOI =          "doi:10.1016/j.aei.2015.07.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1474034615000713",
  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 modelling customer satisfaction for affective
                 design. However, ANFIS is incapable of modelling 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.",
  keywords =     "genetic algorithms, genetic programming, Affective
                 product design, Customer satisfaction, Rough set
                 theory, Particle swarm optimization, ANFIS",
}

Genetic Programming entries for Huimin Jiang Che Kit Kwong K W M Siu Ying Liu

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