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The Scientific World Journal
Volume 2013, Article ID 636948, 11 pages
http://dx.doi.org/10.1155/2013/636948
Research Article

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
2Department of Electrical and Computer Engineering, Curtin University of Technology, Perth, WA 6845, Australia
3School of Design, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Received 24 September 2013; Accepted 21 October 2013

Academic Editors: Z. Cui and X. Yang

Copyright © 2013 C. K. Kwong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Crilly, J. Moultrie, and P. J. Clarkson, “Seeing things: consumer response to the visual domain in product design,” Design Studies, vol. 25, no. 6, pp. 547–577, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. He and D. Wu, “A comparative study of ordinal probit and logistic regression for affective product design,” Advanced Materials Research, vol. 452-453, pp. 642–647, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Liu, “Engineering aesthetics and aesthetic ergonomics: theoretical foundations and a dual-process research methodology,” Ergonomics, vol. 46, no. 13-14, pp. 1273–1292, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. M. E. H. Creusen and J. P. L. Schoormans, “The different roles of product appearance in consumer choice,” Journal of Product Innovation Management, vol. 22, no. 1, pp. 63–81, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. C. H. Noble and M. Kumar, “Using product design strategically to create deeper consumer connections,” Business Horizons, vol. 51, no. 5, pp. 441–450, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Turkyilmaz, A. Oztekin, S. Zaim, and O. F. Demirel, “Universal structure modeling approach to customer satisfaction index,” Industrial Management & Data Systems, vol. 113, no. 7, pp. 932–949, 2013. View at Google Scholar
  7. F. R. Camargo and B. Henson, “Measuring affective responses for human-oriented product design using the Rasch model,” Journal of Design Research, vol. 9, no. 4, pp. 360–375, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Nagamachi, “Kansei engineering as a powerful consumer-oriented technology for product development,” Applied Ergonomics, vol. 33, no. 3, pp. 289–294, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Jiao, Y. Zhang, and M. Helander, “A Kansei mining system for affective design,” Expert Systems with Applications, vol. 30, no. 4, pp. 658–673, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. H. M. Khalid and M. G. Helander, “A framework for affective customer needs in product design,” Theoretical Issues in Ergonomics Science, vol. 5, no. 1, pp. 27–42, 2004. View at Google Scholar
  11. M.-D. Shieh, C.-L. Huang, and C.-C. Yang, “A rough set approach to elicit customer preferences for fashion design,” Journal of Convergence Information Technology, vol. 8, no. 3, pp. 338–349, 2013. View at Google Scholar
  12. L.-K. Chan and M.-L. Wu, “Quality function deployment: a literature review,” European Journal of Operational Research, vol. 143, no. 3, pp. 463–497, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. H.-H. Lai, Y.-M. Chang, and H.-C. Chang, “A robust design approach for enhancing the feeling quality of a product: a car profile case study,” International Journal of Industrial Ergonomics, vol. 35, no. 5, pp. 445–460, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Tarantino, A Statistical Thinking Approach to Kansei Engineering for Product Innovation, University of Naples Federico II, Naples, Italy, 2008.
  15. E. Aktar Demirtas, A. S. Anagun, and G. Koksal, “Determination of optimal product styles by ordinal logistic regression versus conjoint analysis for kitchen faucets,” International Journal of Industrial Ergonomics, vol. 39, no. 5, pp. 866–875, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. H.-H. Lai, Y.-C. Lin, and C.-H. Yeh, “Form design of product image using grey relational analysis and neural network models,” Computers & Operations Research, vol. 32, no. 10, pp. 2689–2711, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. S.-W. Hsiao and H.-C. Tsai, “Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design,” International Journal of Industrial Ergonomics, vol. 35, no. 5, pp. 411–428, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Wan, J. Che, and L. Han, “Car styling perceptual modeling based on fuzzy rules,” Applied Mechanics and Materials, vol. 201-202, pp. 794–797, 2012. View at Google Scholar
  19. H.-C. Tsai, S.-W. Hsiao, and F.-K. Hung, “An image evaluation approach for parameter-based product form and color design,” Computer Aided Design, vol. 38, no. 2, pp. 157–171, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
  21. N. Kasabov, “Evolving neuro-fuzzy inference models,” in Evolving Connectionist Systems: The Knowledge Engineering Approach, pp. 141–176, 2007. View at Google Scholar
  22. L. A. Zadeh and C. Berkeley, Fuzzy Logic Toolbox User’s Guide Version 2, MathWorks, 2001.
  23. N. K. Kasabov and Q. Song, “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144–154, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. N. Kasabov, Q. Song, and T. Ma, “Fuzzy-neuro systems for local and personalized modelling,” in Forging New Frontiers: Fuzzy Pioneers II, pp. 175–197, 2008. View at Publisher · View at Google Scholar
  25. N. Kasabov, “Evolving connectionist methods for unsupervised learning,” in Evolving Connectionist Systems, pp. 53–82, 2007. View at Publisher · View at Google Scholar