Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27482
Title: A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design
Authors: Kwong, CK 
Fung, KY
Jiang, H
Chan, KY
Siu, KWM 
Issue Date: 2013
Publisher: Hindawi Publishing Corporation
Source: The scientific world journal, 2013, v. 2013, 636948 How to cite?
Journal: The scientific world journal 
Abstract: Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
URI: http://hdl.handle.net/10397/27482
ISSN: 2356-6140
EISSN: 1537-744X
DOI: 10.1155/2013/636948
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