Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design. Issue 3 (August 2015)

• Record Type: Journal Article
• Authors: Jiang, Huimin; Kwong, C.K.; Siu, K.W.M.; Liu, Y.
• Is Part Of: Advanced engineering informatics. Volume 29:Issue 3(2015:Aug.)
• Page Start: 727
• Page End: 738
• Publication Date: 2015-08
• Subjects: Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2015.07.005
• Languages: English
• ISSNs: 1474-0346

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st065">Highlights</title> <sec> <p id="sp005"> <list id="l0005"> <list-item id="u0005"> <label></label> <p id="p0210">Rough set and PSO-based ANFIS approaches are proposed to model customer satisfaction in affective product design.</p> </list-item> <list-item id="u0010"> <label></label> <p id="p0215">Rough set is used to simplify ANFIS structure and PSO is introduced to enhance the accuracy of modeling.</p> </list-item> <list-item id="u0015"> <label></label> <p id="p0220">Generated fuzzy rules address fuzziness exists in customer survey data.</p> </list-item> <list-item id="u0020"> <label></label> <p id="p0225">Generated models show the nonlinear relationships between affective responses and design attributes.</p> </list-item> <list-item id="u0025"> <label></label> <p id="p0230">The proposed approaches outperform the FLSR, FR, and GP-FR in terms of training errors and validation errors.</p> </list-item> </list> </p> </sec> </abstract>