Introduction
Chapter 1 defines product design as the transformation of a market opportunity into a product available for sale made possible by product development technology. This transformation is a complex process, as it draws upon and contributes to different domains. Moreover, it is not well formalized. Computational intelligence algorithms fuse historical design information distributed in space and time into coherent and understandable design knowledge (Kusiak and Salustri 2007). This chapter introduces and discusses the recent computational intelligence methods used for product design, which offer modeling methods and optimization algorithms that are developed to design formalization and automation in terms of new product development.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aydin, M.E., Fogarty, T.C.: A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application. Journal of Intelligent Manufacturing 15, 805–814 (2004)
Andes, D., Widrow, B., Lehr, M., Wan, E.: MRIII: A robust algorithm for training abalogy neural network. In: Proc. Int. J. Conf. on Neural Networks 1990 (IJCNN1990), Washington, USA, vol. 1, pp. 533–536 (1990)
Baba, N., Jain, L.C.: Computational intelligence in games. Springer (2001)
Bäck, T., Schwefel, H.P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1, 2–23 (1993)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford, NY (1996)
Back, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation 1(1) (April 1997)
Beyer, H.G.: The Theory of Evolution Strategies. Springer, NY (2001)
Castellano, G., Fanelli, A.M.: An iterative pruning algorithm for feedforward neural networks. IEEE Trans. Neural Networks 8(3), 519–531 (1997)
Chan, K.Y., Kwong, C.K., Tsim, Y.C.: Improved orthogonal array based simulated annealing with interaction analysis between variables for design optimization. Expert Systems with Applications 36, 7379–7389 (2009a)
Chan, K.Y., Kwong, C.K., Tsim, Y.C.: A fuzzy nonlinear regression based on genetic programming to modeling manufacturing processes. International Journal of Production Research 48(7), 1967–1982 (2009b)
Chan, K.Y., Kwong, C.K., Dillon, T.S., Fung, K.Y.: An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness. Journal of Engineering Design 22(3), 523–542 (2010a)
Chan, K.Y., Kwong, C.K., Wong, T.C.: Modelling customer satisfaction for product development using genetic programming. Journal of Engineering Design 22(1), 601–613 (2010b)
Chan, K.Y., Kwong, C.K., Tsim, Y.C., Aydin, M.E., Fogarty, T.C.: A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design. Expert Systems with Applications 37(5), 3853–3862 (2010c)
Cove, P.: IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories (1993)
Couzin, I.D., Krause, J., James, R., Ruxton, G.D., Franks, N.R.: Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology 218, 1–11 (2002)
Das, S., Konar, A.: Swarm intelligence in production management and engineering, Handbook of Computational Intelligence in Manufacturing and Production Management, pp. 345–365 (2008)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Information Theory 36(5), 961–1005 (1990)
Dillon, T.S., Niebur, D.: Neural networks applications in power systems. International Series in Intelligent Systems and their Applications. CRL Publishing (1996)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proc. Congress on Evolutionary Computing, vol. 1, pp. 84–88 (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, Inc., United States of America (1989)
Ham, F.M., Kostanic, I.: Principles of Neurocomputing for Science & Engineering. McGraw-Hill (2001)
Haykin, S.: Neural Network: A Comprehensive Foundation. Prentice-Hall (1999)
Huang, Y.C., Huang, C.M.: Evolving wavelet networks for power transformer condition monitoring. IEEE Trans. Power Delivery 17(2), 412–416 (2002)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79, 2554–2558 (1982)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers (2001)
Kokash, N.: An introduction to heuristic algorithms (2005), http://dit.unitn.it/~kokash/documents/Heuristical
Krause, J., Ruxton, G.D.: Living ingroups. Oxford University Press (2002)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Kozen, D.C.: The design and analysis of algorithms. Springer (1992)
Kwong, C.K., Bai, H.: A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing 13, 367–377 (2002)
Kwong, C.K., Bai, H.: Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Transactions 35, 619–626 (2003)
Kwong, C.K., Wong, T.C., Chan, K.Y.: A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications 36(8), 11262–11270 (2009a)
Kwong, C.K., Chan, K.Y., Tsim, Y.C.: A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging. Expert Systems with Applications 36, 3829–3838 (2009b)
Kwong, C.K., Chen, Y., Chan, K.Y., Luo, X.: A generalized fuzzy least-squares regression approach to modelling functional relationships in QFD. Journal of Engineering Design 21(5), 601–613 (2010)
Kusiak, A., Salustri, F.A.: Computational intelligence in product design engineering: review and trends. IEEE Transactions on Systems, Man and Cybernetics –Part C: Application and Reviews 3(5), 766–788 (2007)
Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)
Peters, G.: Fuzzy linear regression with fuzzy intervals. Fuzz Sets Sys. 63, 45–55 (1994)
Van Laarhoven, P.J.M., Aarts, E.H.L.: Simulated Annealing: Theory and Applications. D. Reidel Publishing Co. (1987)
Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of neural network using an improved genetic algorithm. IEEE Trans. Neural Networks 14(1), 79–88 (2003)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)
Martinelli, D., Prina-Ricotti, L., Ragazzini, S., Mascioli, F.M.: A pyramidal delayed perceptron. IEEE Trans. Circuits Syst. 37, 1176–1181 (1990)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M., Teller, A.H., Teller, E.: Equation of State Calculations by Fast Computing Machines. Journal of Chemistry and Physics 21, 1087–1092 (1953)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4), 525–533 (1993)
Moody, J., Darken, C.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1, 281–294 (1989)
Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Sys. Man Cybern. SMC-12, 903–907 (1982)
Tanaka, H., Watada, J.: Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems 272, 275–289 (1988)
Wai, R.J., Chang, J.M.: Intelligent control of induction servo motor drive via wavelet neural network. Electric Power System Research 61(1), 67–76 (2002)
Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: Perceptron, madaline, and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)
Whitley, D., Starkweather, T., Bogart, C.: Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing 14, 347–361 (1990)
Yao, X.: Evolving artificial networks. Proceedings of the IEEE 87(7), 1423–1447 (1999)
Zimmermann, H.J.: Fuzzy sets theory and its applications. Kluwer, Boston (1985)
Zhao, B., Guo, C.X., Cao, Y.J.: A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Systems 20(2), 1070–1078 (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Berlin Heidelberg
About this paper
Cite this paper
Chan, K.Y., Kwong, C.K., Dillon, T.S. (2012). Computational Intelligence Technologies for Product Design. In: Computational Intelligence Techniques for New Product Design. Studies in Computational Intelligence, vol 403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27476-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-27476-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27475-6
Online ISBN: 978-3-642-27476-3
eBook Packages: EngineeringEngineering (R0)