A 2D Hopfield Neural Network approach to mechanical beam damage detection-Reference-Cited by-同舟云学术

A 2D Hopfield Neural Network approach to mechanical beam damage detection

Published:2015-07-15 Issue:4 Volume:26 Page:1081-1095
ISSN:0923-6082
Container-title:Multidimensional Systems and Signal Processing
language:en
Short-container-title:Multidim Syst Sign Process

Author:

Almeida Juliana,Alonso Hugo,Ribeiro Pedro,Rocha Paula

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software

Link

http://link.springer.com/content/pdf/10.1007/s11045-015-0342-7.pdf

Reference17 articles.

1. Almeida, J., Alonso, H., & Rocha, P. (2013). A 2D neural network approach for beam damage detection. In 8th international workshop on multidimensional systems (nDS13), Erlangen, Deutschland.

2. Almeida, J., Alonso, H., & Rocha, P. (2013). A neural network approach to damage detection in Euler–Bernoulli beams subjected to external forces. In 21st Mediterranean conference on control and automation, Greece.

3. Alonso, H., Mendonça, T., & Rocha, P. (2009). Hopfield neural networks for online parameter estimation. Neural Networks, 22, 450–462.

4. Bauchau, O. A., & Craig, J. I. (2009). Structural analysis (solid mechanics and its applications), Vol. 163 (pp. 173–221). Berlin: Springer.

5. Cao, M., Ye, L., Zhou, L., Zhongqing, S., & Bai, R. (2011). Sensitivity of fundamental mode shape and static deflection for damage identification in cantilever beams. Mechanical Systems and Signal Processing, 25, 630–643.

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