A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design
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 an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/636948 |
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| author | C. K. Kwong K. Y. Fung Huimin Jiang K. Y. Chan Kin Wai Michael Siu |
| author_facet | C. K. Kwong K. Y. Fung Huimin Jiang K. Y. Chan Kin Wai Michael Siu |
| author_sort | C. K. Kwong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f656a0bc343d4b1a89429f2a0d964444 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f656a0bc343d4b1a89429f2a0d9644442025-02-03T06:11:20ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/636948636948A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective DesignC. K. Kwong0K. Y. Fung1Huimin Jiang2K. Y. Chan3Kin Wai Michael Siu4Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Electrical and Computer Engineering, Curtin University of Technology, Perth, WA 6845, AustraliaSchool of Design, The Hong Kong Polytechnic University, Kowloon, Hong KongAffective 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.http://dx.doi.org/10.1155/2013/636948 |
| spellingShingle | C. K. Kwong K. Y. Fung Huimin Jiang K. Y. Chan Kin Wai Michael Siu A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design The Scientific World Journal |
| title | A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design |
| title_full | A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design |
| title_fullStr | A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design |
| title_full_unstemmed | A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design |
| title_short | A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design |
| title_sort | modified dynamic evolving neural fuzzy approach to modeling customer satisfaction for affective design |
| url | http://dx.doi.org/10.1155/2013/636948 |
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