Deep Prediction Networks

Abstract The challenge for next generation system identification is to build new flexible models and estimators able to simulate complex systems. This task is especially difficult in the nonlinear setting. In fact, in many real applications the performance of long-term predictors may be severely affected by stability problems arising due to the output feedback. For this purpose, also the use of deep networks, which are having much success to solve classification problems, has not led so far to any significant cross-fertilization with system identification. This paper proposes a novel procedure based on a hierarchical architecture, which we call deep prediction network, whose flexibility is used to favour the identification of stable systems. In particular, its structure contains layers whose aim is to improve long-term predictions, with complexity controlled by a kernel-based strategy. The usefulness of the new approach is demonstrated through many examples, including important real benchmark problems taken from the system identification literature.