Information Technology Reference
In-Depth Information
In addition to the aspects of training data such as variation of training data set,
sequence of fatigue data and the anticipated bounds of prediction, one may also give
consideration to the initialization of network weights, where classes of evolutionary
algorithms such as genetic algorithms (GA) may be employed in the NN model to
obtain an optimum initialization of network weights. This can result in further
optimization for the current NN models, which is also interesting for the subject of
further study.
Acknowledgment The present author would like to thank the Montana State University and A.P.
Vassilopoulos and T.P. Philippidis (doi:
10.1016/S0142-1123(02)00003-8
)
for the fatigue database
published through the internet. The author also would like to thank to editors and reviewers for
their useful suggestions and comments that further improve the presentation of this research work.
References
Al-Assaf, Y., & El-Kadi, H. (2001). Fatigue life prediction of unidirectional glass
ber/epoxy
composite laminae using neural networks. Composites Structures, 53(6), 65
71.
Aymerich, F., & Serra, M. (1998). Prediction of fatigue strength of composite laminates by means
of neural networks. In The third seminar on experimental techniques and design in composite
Azar, A. T. (2013). Fast neural network learning algorithms for medical applications. Neural
Computing and Applications, 23(3
-
4), 1019
-
1034.
Basso, M., Giarre, L., Groppi, S., & Zappa, G. (2005). NARX models of an industrial power plant
gas turbine. IEEE Transactions on Control System Technology, 13(4), 599
-
604.
Bezazi, A., Pierce, S. G., Worden, K., & Harkati, E. H. (2007). Fatigue life prediction of sandwich
composite materials under
-
flexural tests using a Bayesian trained artificial neural network.
International Journal of Fatigue, 29(4), 738
fl
747.
Bucar, T., Nagode, M., & Fajdiga, M. (2007). An improved neural computing method for
describing the scatter of S-N curves. International Journal of Fatigue, 29(12), 2125
-
2137.
Bukkapatnam, S. T. S., & Sadananda, K. (2005). A genetic algorithm for unified approach-based
predictive modeling of fatigue crack growth. International Journal of Fatigue, 27(10
-
12),
-
1359.
Catelani, M., & Fort, A. (2000). Fault diagnosis of electronic analog circuits using a radial basis
function network classi
er. Measurement, 28(3), 147
1354
-
158.
Chen, S., Billings, S. A., & Grant, P. M. (1990). Non-linear system identi
cation using neural
networks. International Journal of Control, 51(6), 1191
-
1214.
El-Kadi, H., & Al-Assaf, Y. (2002). Prediction of the fatigue life of unidirectional glass
ber/
epoxy composite laminae using different neural network paradigms. Composites Structures, 55
(1), 239
-
246.
Fathi, A., & Aghakouchak, A. A. (2007). Prediction of fatigue crack growth rate in welded tubular
joints using neural network. International Journal of Fatigue, 29(2), 261
-
275.
Foresee, F. D., & Hagan, M. T. (1997). Gauss-Newton approximation to Bayesian learning. In The
1997 IEEE international conference on neural networks (ICNN) (pp. 1930
-
1935), June 9
12,
-
-
1997, Houston, TX, USA. doi:
10.1109/ICNN.1997.614194
.
Freire Junior, R. C. S., Neto, A. D. D., & de Aquino, E. M. F. (2005). Building of constant life
diagrams of fatigue using artificial neural networks. International Journal of Fatigue, 27(7),
746
751.
-
Search WWH ::
Custom Search