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Application of a Hopfield type neural network to the analysis of elastic problems with unilateral constraints

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Polish Conference on Computer methods in mechanics ; (14 ; 26-28.05.1999 ; Rzeszów, Poland
Języki publikacji
EN
Abstrakty
EN
On the base of Hopfield-Tank neural network the Panagiotopoulos approach is briefly discussed. The approach is associated with the analysis of quadratic programming problem with unilateral constraints. Then modifications of this approach are proposed. The original Panagiotopoulos approach is illustrated by the analysis of crack detachment in an elastic body. Efficiency of the proposed modifications is shown on a numerical example of an angular plate. Finally some special conclusions are expressed.
Rocznik
Strony
757--765
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Twórcy
  • Institute of Computer Methods in Civil Engineering, Cracow University of Technology [Politechnika Krakowska], ul. Warszawska 24, 31-155 Kraków, Poland
autor
  • Institute of Computer Methods in Civil Engineering, Cracow University of Technology [Politechnika Krakowska], ul. Warszawska 24, 31-155 Kraków, Poland
Bibliografia
  • [1] A. V. Avdelas et. al. Neural networks for computing in the elastoplastic analysis of structures. M eccanica, 30: 1-15, 1995.
  • [2] G. Engeln-Miillges, F. Uhlig. Numerical Algorithms with C, Springer, Berlin-Heidelberg, 1996.
  • [3] L. Fausett. Fundamentals of Neural Networks - Architectures, Algorithms and Applications. Prentice Hall, Englewood Cliffs, NJ, 1994.
  • [4] S. Haykin. Neural Networks - A Comprehensive Foundation. Macmillan College Publ. Co., New York, 1994.
  • [5] J. J. Hopfield, D. W. Tank. Neural computation of decisions in optimization problems. Biological Cybernetics, 52: 141-152, 1985.
  • [6] J. Korbicz et al. Artificial Neural Networks - Foundations and Applications (in Polish), Akad. Ofic. Wydawn. PLJ, Warszawa, 1994.
  • [7] S. Kortesis, P. G. Panagiotopoulos. Neural networks for computing in structural analysis: methods and prospects of applications. Int. J. Num. Mech. Eng., 36: 2305-2318, 1993.
  • [8] E. S. Mistakidis et al. Delamination of laminated composites under cleavage loading: a Hopfield neural network approach. In: S.A. Paipetis and E.E. Golontes, eds., Proc. 1st Hellenic Conf. on Composite Materials and Structures, 199-133, Xanthi, Greece, 1997.
  • [9] S. Ossowski. Neural Networks (in Polish), Ofic. Wydawn. Polit. Warszawskiej, Warszawa, 1994.
  • [10] P. D. Panagiotopoulos. Hernivariational Inequalities - Applications in Mechanics and Engineering. Springer, Berlin-Heidelberg, 1993.
  • [11] P. S. Theocaris, P.D. Panagiotopoulos. Neural networks for computing in fracture mechanics - methods and prospects of applications. Comp. Meth. Appl. Mech. Eng., 106: 213-228, 1993.
  • [12] P. S. Theocaris, P.D. Panagiotopoulos. Generalized hardening plasticity approximated via anisotropic elasticity: a neural network approach. Comput. Meth. Appl. Mech. Eng., 125: 123-139, 1995.
  • [13] Z. Waszczyszyn (ed.). Neural Networks in the Structural Analysis and Design. CISM Courses and Lectures-No. 404. Springer, Wien-New York, 1999 (in print).
  • [14] Z. Waszczyszyn, C. Cichoń, M. Radwańska. Stability of Structures by Finite Element Methods, Elsevier, Amsterdam, 1994.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB1-0005-0049
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