Development of Product Design Models Using Fuzzy Regression Based Genetic Programming

Part of the Studies in Computational Intelligence book series (SCI, volume 403)

Introduction

To develop a functional model which relates customer requirements to the design attributes of a new product, a set of customer survey data is usually used. As mentioned in Chapter 5, customer survey data is usually fuzzy in nature, as human feeling is usually fuzzy, and also nonlinearities are unavoidable in the relationships between customer requirements and design attributes. However, the development of explicit functional models has not been addressed by previous studies in modelling the relationships between customer requirements and design attributes. Also, those previous modelling methods can address either nonlinearity or fuzziness only. To overcome the deficiencies of the above approaches, this chapter presents a fuzzy regression based genetic programming method, namely FR-GP, to generate functional models which represent this nonlinear and fuzzy relationships between customer requirements and design attributes.

Keywords

Mobile Phone Functional Model Customer Requirement Multivariate Adaptive Regression Spline Design Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Artacho, M.A., Ballester, A., Alcantara, E.: Analysis of the impact of slight changes in product formal attributes on user’s emotions and configuration of an emotional space for successful design. Journal of Engineering Design (2009)Google Scholar
  2. Barnes, C., Lillford, S.P.: Decision support for the design of affective products. Journal of Engineering Design 20(5), 477–492 (2009)CrossRefGoogle Scholar
  3. Chan, K.Y., Kwong, C.K., Tsim, Y.C.: Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms. Engineering Applications of Artificial Intelligence 23(1), 18–26 (2010)CrossRefGoogle Scholar
  4. Chen, C.H., Khoo, L.P., Yan, W.: An investigation into affective design using sorting technique and Kohonen self-organizing map. Advances in Engineering Software 37, 334–349 (2006)CrossRefGoogle Scholar
  5. Chuang, M.C., Ma, Y.C.: Expressing the expected product images in product design of micro-electronic products. International Journal of Industrial Ergonomics 27(4), 233–245 (1999)CrossRefGoogle Scholar
  6. Cross, N.: Engineering design methods: Strategies for product design, 3rd edn. Wiley, Chichester (2000)Google Scholar
  7. Eggink, W.: A practical approach to teaching abstract product design issues. Journal of Engineering Design 20(5), 511–521 (2009)CrossRefGoogle Scholar
  8. Girard, S., Johnson, H.: Developing affective educational software products: Soremo, a new method for capturing emotional states. Journal of Engineering Design 20(5), 493–510 (2009)CrossRefGoogle Scholar
  9. Desmet, P.M.A.: Designing emotions. Delft University of Technology, Delft (2002)Google Scholar
  10. Diener, E., Lucas, R.E.: Subjective emotional well-being. In: Lewis, M., Haviland-Jones, J.M. (eds.) Handbook of Emotions, 2nd edn., pp. 325–337. The Guilford Press, NewYork (2000)Google Scholar
  11. Friedman, J.H.: Multivariate Adaptive Regression Splines. The Annals of Statistics 19(1), 1–141 (1991)MathSciNetMATHCrossRefGoogle Scholar
  12. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the 5th International Conference in Genetic Algorithms (1993)Google Scholar
  13. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, Inc., United States of America (1989)MATHGoogle Scholar
  14. Gu, Z., Tang, M.X., Frazer, J.H.: Capturing aesthetic intention during interactive evolution. Computer-Aided Design 38, 224–237 (2006)CrossRefGoogle Scholar
  15. Han, S.H., Hong, S.W.: A systematic approach for computing user satisfaction with product design. Ergonomics 46(13), 1441–1461 (2003)CrossRefGoogle Scholar
  16. Han, S.H., Yun, M.H., Kim, K., Kwahk, J.: Evolution of product usability: development and validation of usability dimensions and design elements based on empirical models. International Journal of Industrial Ergonomics 26, 477–488 (2000)CrossRefGoogle Scholar
  17. Hsiao, S.W., Liu, M.C.: A morphing method for shape generation and image prediction in product design. Design Studies 23(5), 497–513 (2002)Google Scholar
  18. Hsiao, S.W., Tsai, H.C.: Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics 35, 411–428 (2005)CrossRefGoogle Scholar
  19. Jiao, J.: A Kansei mining system for affective design. Expert Systems with Applications 30, 658–673 (2006)CrossRefGoogle Scholar
  20. Jindo, T., Hirasago, K.: Application studies to car interior of Kansei engineering. International Journal of Industrial Ergonomics 19, 105–114 (1997)CrossRefGoogle Scholar
  21. Jordan, P.W.: Designing pleasurable products: an introduction to the new human factors. Taylor & Francis, London (2000)CrossRefGoogle Scholar
  22. Kesteren, I., Bruijn, S., Stappers, P.J.: Evaluation of materials selection activites in user-centred design projects. Journal of Engineering Design 19(5), 417–429 (2008)CrossRefGoogle Scholar
  23. Khalid, H.M.: Towards affective collaborative design. In: Smith, M.J., Salvendy, G., Harris, D., Koubek, R.J. (eds.) Proceedings of HCI International 2001. Usability Evaluation and Interface Design, vol. 1, Lawrence Erlbaum, Mahwah (2001)Google Scholar
  24. Kim, K.J., Moskowitz, H., Koksalan, M.: Fuzzy versus statistical linear regression. European Journal of Operational Research 92, 417–434 (1996)MATHCrossRefGoogle Scholar
  25. Kouprie, M., Visser, F.S.: A framework for empathy in design: stepping into and out of the user’s life. Journal of Engineering Design 20(5), 437–448 (2009)CrossRefGoogle Scholar
  26. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Evolution. MIT Press, Cambridge (1992)Google Scholar
  27. Knowles, J.D., Corne, D.W.: Approximating the non-dominated front using the pareto archived evolution strategy. Evolutionary Computation 8, 149–172 (2000)CrossRefGoogle Scholar
  28. Koza, J.: Genetic Programming II: automatic discovery of reusable programs. MIT Press (1994)Google Scholar
  29. Kuang, J., Jiang, P.: Product platform design for a product family based on Kansei engineering. Journal of Engineering Design (2008)Google Scholar
  30. Lai, H.H., Lin, Y.C., Yeh, C.H.: Form design of product image using grey relational analysis and neural network models. Computers & Operations Research 32(10), 2689–2711 (2004)CrossRefGoogle Scholar
  31. Lau, T.W., Hui, C.L., Ng, S.F., Chan, C.C.: A new fuzzy approach to improve fashion product development. Computers in Industry 57, 82–92 (2006)CrossRefGoogle Scholar
  32. Madar, J., Abonyi, J., Szeifert, F.: Genetic programming for the identification of nonlinear input – output models. Industrial and Engineering Chemistry Research 44, 3178–3186 (2005)CrossRefGoogle Scholar
  33. Mugge, R., Schoormans, J.P.L., Schifferstein, N.J.: Emotional bonding with personalized products. Journal of Engineering Design 20(5), 467–476 (2009)CrossRefGoogle Scholar
  34. Nagamachi, M.: Kansei Engineering: A new ergonomic consumer-oriented technology for product development 15, 3–11 (1995)Google Scholar
  35. Nurkka, P., Kujala, S., Kemppainen, K.: Capturing user’s perceptions of valuable experience and meaning. Journal of Engineering Design 20(5), 449–463 (2009)CrossRefGoogle Scholar
  36. Nikolaev, N.I., Iba, H.: Accelerated Genetic Programming of Polynomials. Genetic Programming and Evolvable Machines 2, 231–257 (2001)MATHCrossRefGoogle Scholar
  37. Norman, D.A.: Emotional design: why we love (or hate) everyday things. Basic Books, New York (2004)Google Scholar
  38. Park, J., Han, S.H.: A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics 34, 31–47 (2004)CrossRefGoogle Scholar
  39. Peters, G.: Fuzzy linear regression with fuzzy intervals. Fuzzy Sets and Systems 63, 45–55 (1994)MathSciNetCrossRefGoogle Scholar
  40. Petiot, J.F., Grognet, S.: Product design: a vectors field-based approach for preference modeling. Journal of Engineering Design 17(3), 217–233 (2006)CrossRefGoogle Scholar
  41. Seber, G.A.F.: Linear regression analysis. Wiley (2003)Google Scholar
  42. Shimizu, Y., Jindo, Y.: A fuzzy logic analysis method for evaluation human sensitivities. International Journal of Industrial Ergonomics 15, 39–47 (1995)CrossRefGoogle Scholar
  43. Tanoue, C., Ishizaka, K., Nagamachi, M.: Kansei engineering: a study on perception of vehicle interior image. International Journal of Industrial Ergonomics 19, 115–128 (1997)CrossRefGoogle Scholar
  44. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)MATHGoogle Scholar
  45. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Transactions on Systems, Man, and Cybernetics 12, 903–907 (1982)MATHCrossRefGoogle Scholar
  46. Tanaka, H., Watada, J.: Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems 272, 275–289 (1988)MathSciNetCrossRefGoogle Scholar
  47. Yang, S., Nagamachi, M., Lee, S.: Rule based inference model for the Kansei engineering system. International Journal of Industrial Ergonomics 24, 459–471 (1999)CrossRefGoogle Scholar
  48. You, H., Taebeum, R.Y.U., Kyunghee, O.H., Yun, M.H., Kim, K.J.: Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics 36, 323–330 (2006)CrossRefGoogle Scholar
  49. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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