Meta-Learning for Adaptive Identification of Non-Linear Dynamical Systems

Adaptive identification of non-linear dynamical systems via recurrent neural networks (RNNs) is presented in this paper. We explore the notion that a fixed-weight RNN needs to change only its internal state to change its behavior policy. This ability is acquired through prior training procedure that enable the learning of adaptive behaviors. Some simulation results are presented

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