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NN-based damage detection in multilayer composites
- Authors
- Wei, Zhi; Hu, Xiaomin; Fan, Muhui; Zhang, Jun; Bi, D.
- Issue Date
- Aug-2005
- Publisher
- Springer Verlag
- Citation
- Advances in Natural Computation First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II, v.3611, no.PART II, pp 592 - 601
- Pages
- 10
- Indexed
- SCI
SCOPUS
- Journal Title
- Advances in Natural Computation First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II
- Volume
- 3611
- Number
- PART II
- Start Page
- 592
- End Page
- 601
- 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. © Springer-Verlag Berlin Heidelberg 2005.
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Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)