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Recent developments within deep learning are relevant for nonlinear systemidentification problems. In this paper, we establish connections between thedeep learning and the system identification communities. It has recently beenshown that convolutional architectures are at least as capable as recurrentarchitectures when it comes to sequence modeling tasks. Inspired by theseresults we explore the explicit relationships between the recently proposedtemporal convolutional network (TCN) and two classic system identificationmodel structures; Volterra series and block-oriented models. We end the paperwith an experimental study where we provide results on two real-world problems,the well-known Silverbox dataset and a newer dataset originating from groundvibration experiments on an F-16 fighter aircraft.

Deep Convolutional Networks in System Identification