Deep transfer learning for system identification using long short-term memory neural networks
This work addresses data and computation efficiency for system identification in dynamical systems, but it is incremental as it builds on existing LSTM methods.
The paper tackled the problem of high data and computation requirements in system identification using LSTM neural networks by proposing deep transfer learning techniques, resulting in a 10% to 50% acceleration in learning and savings in resources.
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.