[1] Mamalis W, Johnson ,W. "The quasi-static crumpling of thin-wall circular
cylinders and frusta under axial compression ." Int . J .Mech Sci 1983 [2]
Mamalis W, Manolakos DE , Saigal S , Viegelahn G ,Johnson W
,"Extensible plastic collapse of thin-wall frusta as energy absorbers ". Int . J
.Mech Sci 1986(219-29)
[3] Wierzbicki .T,Abramowicz . W ."On the crushing mechanics of thin -walled structures ". Int .J .Appl Mech1989;56(1):113-20.
[4] Astrom k.g. and Eykhoff “System identification a survey” Automatica
7-123-62,1971
[5] Sanchez E.; T. Shibata; and L. A. Zadeh. 1997. “Genetic Algorithms and
Fuzzy Logic Systems” World Scientific.
[6] Farlow S.J. ed. 1984. “Self-organizing Method in Modelling: GMDH
type algorithm” Marcel Dekker Inc.
[7] Ivakhnenko A.G. 1971. “Polynomial Theory of ComplexSystems” IEEE
Trans. Syst. Man & Cybern, MC-1, 364-378.
[8] Nariman-Zadeh, N.; A. Darvizeh; and R. Ahmad-Zadeh; 2003.“Hybrid
Genetic Design of GMDH-Type Neural Networks Using Singular Value
Decomposition for Modeling and Prediction of the Explosive Cutting
Process” Proceedings of the I MECH E Part B Journal of Engineering
Manufacture, Vol. 217, pp. 779 -790.
[9] Coello, C.A., "A comprehensive survey of evolutionary based multi-objective optimization techniques", Knowledge and Information Systems: An
Int. Journal, (3), pp 269-308, 1999.
[10] Goldberg, D. E., "Genetic Algorithms in Search, Optimization, and
Machine Learning", Addison- Wesley, New York, 1989.
[11] Deb, K., Agrawal, S., Pratap, A., Meyarivan, T., "A fast and elitist multi -bjective genetic algorithm: NSGAII", IEEE Trans. On Evolutionary
Computation, 6(2), 182-197, 2002.
[12] K. Atashkari, N. Nariman-zadeh A. Khalkhali A. Jamali “Modelling and
Multi-objective Optimization of a Variable Valve-timing Sparkignition
Engine using Polynomial Neural Networks and Evolutionary Algorithms”
Journal of Energy Conversion and Management
[13] M. Alitavoli, Nariman-zadeh A. Khalkhali M. Mehran “Modeling of
abrasive fluid micro machining parameters by polynomial neural networks
and genetic algorithms” Accepted in MESM Conf October 24-26, 2005,
Porto, Portugal
[14] On the response of thin-walled CFRP composite tubular components
subjected to static and dynamic axial compressive loading: experimental ,
A.G.Mamalis, D.E. Manolakos, M.B. Ioannidis, D.P. Papapostolou ,
Composite Structures 69 (2005) 407–420
[15] M. Alitavoli, Nariman-zadeh A. Khalkhali M. Mehran “Modeling of
abrasive fluid micro machining parameters by polynomial neural networks
and genetic algorithms” Accepted in MESM Conf October 24-26, 2005,
Porto, Portugal
[16] Johnson RG, Cook WH. A constitutive model and data for metals
subjected to large strains, high strain-rates and high temperature. In: Proc 7th
intsymp ball the hague the Netherlands; 1983. p. 541–47.
[17] A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi, and S.
Hamrang, Multi-objective evolutionary optimization of polynomial neural
networks for modelling and prediction of explosive cutting process, Int. J.
Eng. Appl. Artif. Intell
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