Parametric system identification using deep convolutional neural networks

As industrial systems become more complex with increasing connectivity, there has been a growing demand for scallable and robust methods for identification of hundreds to thousands system parameters from large time-series data (i.e., ordered measurements). In the past, Artificial Neural Networks (ANNs) have been used successfully in system identification and controls of various systems such as magnetic levitation. Recent literature revealed how ANNs can also be used directly for extracting parametric information from dynamic systems. However, scalability of these approaches to complex systems, their robustness to sensor noise have not been well-studied for system identification of closed-loop control systems. This paper addresses the scalability and robustness issues in complex nonlinear system identification of dynamic system within the framework of deep convolutional neural networks (CNNs). CNNs provide means to model local receptive fields, shared weights, and spatial or temporal subsampling, and some of the robustness issues. In addition, CNN weights are learned with back-propagation with respect to minimizing a loss function. Therefore, the weights are synthesized similar to traditional prediction error methods without the need to manually configure hundreds and thousands of ordinary differential equations. In this paper, a new deep CNN architecture to overcome scalability and robustness issues is proposed. The new deep CNN architecture is implemented in Keras with Tensorflow backend, and applied to building energy load prediction problem.