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