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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 |
Issue Date: | 2013 | Source: | Procedia computer science, 2013, v. 22, p. 104-112 | 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. | Keywords: | Affective design ANFIS Customer satisfaction Particle swarm optimization Rough set theory |
Publisher: | Elsevier | Journal: | Procedia computer science | ISSN: | 1877-0509 | DOI: | 10.1016/j.procs.2013.09.086 | Description: | 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013, Kitakyushu, 9-11 September 2013 | 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 |
Appears in Collections: | Conference Paper |
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