  Recurrent neural networks (RNNs) have many advantages over more traditional
system identification techniques. They may be applied to linear and nonlinear
systems, and they require fewer modeling assumptions. However, these neural
network models may also need larger amounts of data to learn and generalize.
Furthermore, neural networks training is a time-consuming process. Hence,
building upon long-short term memory neural networks (LSTM), this paper
proposes using two types of deep transfer learning, namely parameter
fine-tuning and freezing, to reduce the data and computation requirements for
system identification. We apply these techniques to identify two dynamical
systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear
system. Results show that compared with direct learning, our method accelerates
learning by 10% to 50%, which also saves data and computing resources.
