Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80112
Title: Development of customer satisfaction models for affective design using rough set and ANFIS approaches
Authors: Jiang, H 
Kwong, CK 
Law, MC
Ip, WH 
Keywords: Affective design
ANFIS
Customer satisfaction
Particle swarm optimization
Rough set theory
Issue Date: 2013
Publisher: Elsevier
Source: Procedia computer science, 2013, v. 22, p. 104-112 How to cite?
Journal: Procedia computer science 
Abstract: Rough set (RS)- and particle swarm optimization (PSO)- based adaptive neuro-fuzzy inference system (ANFIS) approaches are proposed to generate customer satisfaction models in affective design that address fuzzy and nonlinear relationships between affective responses and design attributes. The RS theory is adopted to reduce the number of fuzzy rules generated using ANFIS and simplify the structure of ANFIS. PSO is employed to determine the parameter settings of an ANFIS from which customer satisfaction models with better modeling accuracy can be generated. A case study of mobile phone affective design is used to illustrate the proposed approaches.
Description: 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013, Kitakyushu, 9-11 September 2013
URI: http://hdl.handle.net/10397/80112
ISSN: 1877-0509
DOI: 10.1016/j.procs.2013.09.086
Rights: © 2013 The Authors. Published by Elsevier B.V. Open access under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and peer-review under responsibility of KES International.
The following publication Jiang, H., Kwong, C. K., Law, M. C., & Ip, W. H. (2013). Development of customer satisfaction models for affective design using rough set and ANFIS approaches. Procedia computer science, 2013, 22, 104-112 is available at https://dx.doi.org/10.1016/j.procs.2013.09.086
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