LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes

With the rapid development in sensor and measurement technology, time-series data of processes have become available in large amounts with high accuracy. Machine learning and data science play an important role in analyzing and perceiving information of the underlying process dynamics from these data. Building a model describing the dynamics is vital in designing and optimizing various processes, as well as predicting their long-term transient behavior. Inferring a dynamic process model from data, often called system identification, has a rich history; see, e.g., [30,46]. While linear system identification is well established, nonlinear system identification is still far from being as good understood as for linear systems, despite having a similarly long research history, see, e.g., [25, 44]. Nonlinear system identification often relies on a good hypothesis of the model; thus, it is not entirely a black-box technology. Fortunately, there are several scenarios where one can hypothesize a model structure based on a good understanding of the underlying dynamic behavior using expert knowledge or experience. Towards nonlinear system identification, a promising approach based on a symbolic regression was proposed [4] to determine the potential structure of a nonlinear system.