Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks

Author:

Nascimento Samy,Valdenegro-Toro Matias

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference11 articles.

1. Caccia, M., Bono, R., Bruzzone, G., Bruzzone, G., Spi-randelli, E., and Veruggio, G. (2001). Experiences on Actuator Fault Detection, Diagnosis and Accommodation for ROVs. In 12th International Symposium on Unmanned Untethered Submersible Technology.

2. Nonlinear system identification using neural networks;Chen;International Journal of Control,1990

3. Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. doi:10.3115/v1/W14-4012. URL http://arxiv.org/abs/1409.1259.

4. Goodfellow, I.J., Bengio, Y., and Courville, A. (2017). Deep Learning. doi:10.1038/nmeth.3707.

5. Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. 385. doi:10.1007/978-3- 642-24797-2.

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