Development of customer satisfaction models for affective design using rough set and ANFIS approaches

Huimin Jiang; Chun Kit Kwong; M. C. Law; W. H. Ip

doi:10.1016/j.procs.2013.09.086

Development of customer satisfaction models for affective design using rough set and ANFIS approaches

Huimin Jiang, Chun Kit Kwong, M. C. Law, W. H. Ip

Research output: Journal article publication › Conference article › Academic research › peer-review

9 Citations (Scopus)

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.

Original language	English
Pages (from-to)	104-112
Number of pages	9
Journal	Procedia Computer Science
Volume	22
DOIs	https://doi.org/10.1016/j.procs.2013.09.086
Publication status	Published - 1 Jan 2013
Event	17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013 - Kitakyushu, Japan Duration: 9 Sept 2013 → 11 Sept 2013

Keywords

Affective design
ANFIS
Customer satisfaction
Particle swarm optimization
Rough set theory

ASJC Scopus subject areas

General Computer Science

More information

10.1016/j.procs.2013.09.086

http://hdl.handle.net/10397/80112

Cite this

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title = "Development of customer satisfaction models for affective design using rough set and ANFIS approaches",

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",

author = "Huimin Jiang and Kwong, {Chun Kit} and Law, {M. C.} and Ip, {W. H.}",

year = "2013",

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doi = "10.1016/j.procs.2013.09.086",

language = "English",

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AU - Jiang, Huimin

AU - Kwong, Chun Kit

AU - Law, M. C.

AU - Ip, W. H.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

KW - Affective design

KW - ANFIS

KW - Customer satisfaction

KW - Particle swarm optimization

KW - Rough set theory

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U2 - 10.1016/j.procs.2013.09.086

DO - 10.1016/j.procs.2013.09.086

M3 - Conference article

SN - 1877-0509

VL - 22

SP - 104

EP - 112

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013

Y2 - 9 September 2013 through 11 September 2013

ER -

Development of customer satisfaction models for affective design using rough set and ANFIS approaches

Abstract

Keywords

ASJC Scopus subject areas

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