Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32804
Title: A neuro-fuzzy approach to generating customer satisfaction model for new product development
Authors: Kwong, CK 
Wong, TC
Keywords: ANFIS
Customer satisfaction model
Explicit model
Issue Date: 2008
Publisher: IEEE
Source: IEEE International Conference on Industrial Engineering and Engineering Management, 2008 : IEEM 2008, 8-11 December 2008, Singapore, p. 1804-1808 How to cite?
Abstract: Understanding customer perception towards consumer products is of extremely important to design teams for designing new products. It is because success of new products is heavily dependent on the associated customer satisfaction level. If the consumers are satisfied with a new product, the chance of the product to be successful in a marketplace would be higher. In this study, we applied adaptive neuro-fuzzy inference system (ANFIS) to generate customer satisfaction models based on market survey data. A modified ANFIS (M-ANFIS) is proposed by which explicit customer satisfaction models can be generated. The models can efficiently deal with continuous input values instead of crispy numbers. To justify M-ANFIS, it was compared with a well-known statistical method, multiple linear regression (MLR). Experimental results indicated that the M-ANFIS outperformed MLR in terms of mean absolute errors and variance of errors.
URI: http://hdl.handle.net/10397/32804
ISBN: 978-1-4244-2629-4
978-1-4244-2630-0 (E-ISBN)
DOI: 10.1109/IEEM.2008.4738183
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
0
Last month
Citations as of Apr 10, 2016

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
0
Citations as of Aug 16, 2017

Page view(s)

38
Last Week
2
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.