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Computational intelligence in manufacturing quality control

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Inteligencja obliczeniowa w sterowaniu jakością wytwarzania
Języki publikacji
EN
Abstrakty
EN
The field of Computational Intelligence (Cl) comprises well established technologies of neural networks, fuzzy systems. evolutionary computation and other adaptive and biologically motivated computational paradigms. Support Vector Machines (SVMs) and Kernel Methods (KM) are emerging fields among others. We look at one of these technigues (SVMs), in the setting of a defects detection model of automobile plastic parts within the framework of a manufacturing quality control problem.
PL
W pracy omówiono dziedzinę sztucznej inteligencji zwaną Techniką Wektorów Podtrzymujących (ang. Support Vector Machines, SVMs) z wykorzystaniem Metod Funkcji Jądra (ang. Kernel Methods. KM). Przedstawiono problem fabrycznej kontroli jakości jako przykład zadania klasyfikacji. Wyniki ilustrowane są praktycznym wykorzystaniem SMVs do wykrywania uszkodzeń w częściach samochodowych wyprodukowanych z tworzyw sztucznych.
Rocznik
Strony
286--290
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
  • Department of Informatics Engineering, University of Coimbra, Portugal
Bibliografia
  • [1] D. B. Fogel and C. J. Robinson, eds., Computational Intelligence : The Experts Speak. Wiley-IEEE Press, June 2003. 304 pages.
  • [2] W. Pedrycz, “Computational intelligence: An introduction,”in Computational Intelligence and Applications (P. Szczepaniak, ed.), pp. 3—17, Cambridge,MA: Physica-Verlag, 1999.
  • [3] P. J. Lisboa, “Industrial use of safety-related artificial neural networks,” Technical Report HSE CONTRACT RESEARCH REPORT 327/2001, School of Computing and Mathematical Sciences John Moores University, Liverpool, UK, 2001.
  • [4] L. Zadeh, “From computing with numbers to computing with words — from manipulation of measurements to manipulation of perceptions,” IEEE Transactions on Circuits and Systems, no. 45, pp. 105—119, 1999.
  • [5] X. Yao, “An overview of evolutionary computation,” Chinese Journal of Advanced Software Research, vol. 3, no. 1, pp. 12— 29, 1996.
  • [6] V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer Verlag, 1995.
  • [7] K. Jonsson, J. Kittler, Y. Li, and J. Matas, “Support vector machines for face authentication,” in Proc. of BMVC’99 (T. Pridmore and D. Elliman, eds.), pp. 543—553, 1999.
  • [8] J. T.-Y. Kwok, “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1162—1173, 2000.
  • [9] B. Ribeiro and P. Carvalho, “Mercer’s kernel based learning for fault detection,” in Soft Computing Systems - Design, Management and Applications (A. Abraham, J. Ruiz-del-Solar, and M. Köppen, eds.), Frontiers in Artificial Intelligence and Applications Vol. 87, pp. 341—350, IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington D.C., 2002.
  • [10] V. Vapnik and A. Chervonenkis, “Uniform convergence of frequencies of occurrence of events to their probabilities,” Dokl. Akad. Nauk SSSR, vol. 181, pp. 915 — 918, 1968.
  • [11] F. Cucker and S. Smale, “On the mathematical foundations of learning,” Bulletin of the American Mathematical Society, vol. 39, no. 1, pp. 1—49, 2001.
  • [12] C. Cortes and V. Vapnik, “Support vector networks,”Machine Learning”, vol. 20, pp.273—297, 1995.
  • [13] T. Poggio and F. Girosi, “Regularization algorithms for learning that are equivalent to multilayer networks,” Science, vol. 247, pp. 978—982, 1990.
  • [14] V. Cherkassy, X. Shao, F. M. Mulier, and V. Vapnik, “Model complexity control for regression using VC generalisation bounds,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1075—1089, 1999.
  • [15] A. Ypma, D. Tax, and R. Duin, “Robust machine fault detection with independent component analysis and support vector description,” pp. 1991—1999, 1999.
  • [16] A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, “Support vector clustering,” Journal of Machine Learning Research, no. 2, pp. 125—137, 2001.
  • [17] N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
  • [18] C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121—167, 1998.
  • [19] M. Pontil and A. Verri, “Properties of support vector machines,” Neural Computation, vol. 10, pp. 955—974, 1997.
  • [20] T. Evgeniou, M. Pontil, and T. Poggio, “Regularization networks and support vector machines,”Advances in Computational Mathematics, vol. 13, no. 1, pp. 1—50, 2000.
  • [21] T. Mercer, “Functions of positive and negative and their connections to the theory of integral equations,” Trans. London Phil. Soc. (A), vol. 209, pp. 415—446, 1909.
  • [22] R. Courant and D. Hilbert, Methods of Mathematical Physics I. Springer-Verlag, 1924.
  • [23] J. Weston and C. Watkins, “Multi-class support vector machines,” Tech. Rep. CSD-TR-98-04, Royal Holloway, University of London, Egham, TW20 0EX, UK, 1998.
  • [24] U. H.-G. Kreßel, “Pairwise classification and support vector machines,” in Advances in Kernel Methods: Support Vector Learning (B-Schölkopf, C. Burges, and A. Smola, eds.), pp. 255—268, Cambridge, MA: MIT Press, 1999.
  • [25] “PLASFIL- Plasticos da Figueira, LDA.” Zona Industrial da Gala, Apartado 51,3081-85 Figueira da Foz, Portugal.
  • [26] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” tech. rep., Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2000
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0005-0084
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