Reference : System Identification of a Vertical Riser Model with Echo State Networks
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36468
System Identification of a Vertical Riser Model with Echo State Networks
English
Antonelo, Eric Aislan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Camponogara, Eduardo [> >]
Plucenio, Agustinho [> >]
2015
IFAC-PapersOnLine
48
6
304-310
Yes (verified by ORBilu)
International
2405-8963
2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production OOGP 2015
[en] Echo State Networks ; system identification ; reservoir computing ; vertical riser
[en] System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks (RNNs) trained with back-propagation through time. The recently introduced Reservoir Computing (RC)∗∗The term reservoir used here is not related to reservoirs in oil and gas industry. approach to training RNNs is a viable and powerful alternative which renders fast training and high performance. In this work, a single Echo State Network (ESN), a flavor of RC, is employed for system identification of a vertical riser model which has stationary and oscillatory signal behaviors depending of the production choke opening input variable. It is shown experimentally that these different behaviors are learned by constraining the high-dimensional reservoir states to attractor subspaces in which the specific behavior is represented. Further experiments show the stability of the identified system.
http://hdl.handle.net/10993/36468
10.1016/j.ifacol.2015.08.048
http://www.sciencedirect.com/science/article/pii/S2405896315009155

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