Produção Nacional
System Identification of a Vertical Riser Model with Echo State Networks∗1This work was partially funded by CNPq under grants 471978/2013-2 and 501507/2013-2.
2015; Elsevier BV; Volume: 48; Issue: 6 Linguagem: Inglês
10.1016/j.ifacol.2015.08.048
ISSN2405-8971
AutoresEric Aislan Antonelo, Eduardo Camponogara, Agustinho Plucenio,
Tópico(s)Advanced Memory and Neural Computing
ResumoSystem 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)∗ 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.
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