[1]
B. Harris, (ed. ), Fatigue in Composites, Woodhead Publishing Ltd, Cambridge, England, (2003).
Google Scholar
[2]
Aymerich, F., Serra, M., Prediction of Fatigue Strength of Composite Laminates by Means of Neural Networks, Key Engineering Materials, Vol. 144, 1998, pp.231-240.
DOI: 10.4028/www.scientific.net/kem.144.231
Google Scholar
[3]
J.A. Lee, D.P. Almond, A Neural-network approach to fatigue life prediction, Fatigue in Composites, edited by B. Harris, Woodhead Publishing Ltd, Cambridge, England, 2003, pp.569-589.
DOI: 10.1533/9781855738577.4.569
Google Scholar
[4]
Y. Al-Assaf, H. El-Kadi, Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks, Composites Structures, Vol. 53, No. 6, pp.65-71, (2001).
DOI: 10.1016/s0263-8223(00)00179-3
Google Scholar
[5]
H. El-Kadi, Y. Al-Assaf, Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms, Composites Structures, Vol. 55, No. 1, pp.239-246, (2002).
DOI: 10.1016/s0263-8223(01)00152-0
Google Scholar
[6]
R.C.S. Freire Junior, A.D.D. Neto, and E.M.F. de Aquino, Building of constant life diagrams of fatigue using artificial neural networks, International Journal of Fatigue, Vol. 27, No. 7, pp.746-751, (2005).
DOI: 10.1016/j.ijfatigue.2005.02.003
Google Scholar
[7]
R.C.S. Freire Junior, A.D.D. Neto, and E.M.F. de Aquino, Use of modular networks in the building of constant life diagrams, International Journal of Fatigue, Vol. 29, No. 3, pp.389-396, (2007).
DOI: 10.1016/j.ijfatigue.2006.06.005
Google Scholar
[8]
R.C.S. Freire Junior, A.D.D. Neto, and E.M.F. de Aquino, Comparative study between ANN models and conventional equations in the analysis of fatigue failure of GFRP, International Journal of Fatigue, Vol. 31, No. 5, pp.831-839, (2009).
DOI: 10.1016/j.ijfatigue.2008.11.005
Google Scholar
[9]
A.P. Vassilopoulos, E.F. Georgopoulos, and V. Dionysopoulos, Artificial neural networks in spectrum fatigue life prediction of composite materials, International Journal of Fatigue, Vol. 29, No. 3, pp.20-29, (2007).
DOI: 10.1016/j.ijfatigue.2006.03.004
Google Scholar
[10]
M. I. P. Hidayat, P. S. M. Megat-Yusoff, and W. Berata, Neural networks with NARX structure for material lifetime assessment application, IEEE Symposium on Computers and Informatics, March 20-22, 2011, Kuala Lumpur, Malaysia, in press.
DOI: 10.1109/isci.2011.5958926
Google Scholar
[11]
M. Catelani, A. Fort, Fault diagnosis of electronic analog circuits using a radial basis function network classifier, Measurement, Vol. 28, p.147–158, (2000).
DOI: 10.1016/s0263-2241(00)00008-7
Google Scholar
[12]
S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson Prentice Hall, USA, (2009).
Google Scholar
[13]
A.P. Vassilopoulos, B.D. Manshadi, and T. Keller, Influence of the constant life diagram formulation on the fatigue life prediction of composite materials, International Journal of Fatigue, Vol. 32, No. 4, pp.659-669, (2010).
DOI: 10.1016/j.ijfatigue.2009.09.008
Google Scholar
[14]
S. Chen, S. A. Billings and P. M. Grant, Non-linear system identification using neural networks, International Journal of Control, Vol. 51, No. 6, pp.1191-1214, (1990).
Google Scholar
[15]
K. Narendra and K. Parthasarathy, Identification and control of dynamic systems using neural networks, IEEE Transactions on Neural Networks, Vol. 1, No. 1, p.4–27, (1990).
Google Scholar
[16]
Neural Network Toolbox™ User's Guide © COPYRIGHT 1992–2010 by The MathWorks, Inc.
Google Scholar
[17]
DOE/MSU Composite Material Fatigue Database, Montana State University, (2010).
Google Scholar
[18]
A.P. Vassilopoulos and T.P. Philippidis, Complex stress state effect on fatigue life of GRP laminates. Part I, experimental, International Journal of Fatigue, Vol. 24, No. 8, 2002, pp.813-823.
DOI: 10.1016/s0142-1123(02)00003-8
Google Scholar
[19]
F.D. Foresee, M.T. Hagan, Gauss-Newton approximation to bayesian learning, IEEE International Conference on Neural Networks, Vol. 3, No. 8, pp.1930-1935, (1997).
DOI: 10.1109/icnn.1997.614194
Google Scholar
[20]
D.J.C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, England, (2004).
Google Scholar