Abstract
The discrete-time system of multilayer composite plate is modeled using neural network (NN) to produce a nonlinear exogenous autoregressive moving-average model (NARMAX). The model is implemented by training a NN with input-output experimental data. Each damaged sample can be modeled by a parameter governed by the propagation behaviors of the NN. A residual signal is evaluated from the difference between the output of the model and that of the real system. A threshold function is used to detect the damaged behavior of the system. The results show that a three-layer neural network can be a general type of and suitable for the nonlinear input-output mapping problems of multilayer composite system.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wei, Z., Hu, X., Fan, M., Zhang, J., Bi, D. (2005). NN-Based Damage Detection in Multilayer Composites. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_84
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DOI: https://doi.org/10.1007/11539117_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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