Shengbing Tang
ABSTRACT
The Gaussian process (GP) has been widely used to learn the dynamical system from training data. Current methods ignore the dependencies among the multiple dimensions of the system function and model each dimension of the system function using an independent single-output GP. This paper proposes a novel multiple-output Gaussian process model to learn the dynamical system. We assume that ordinary differential equations (ODEs) with unknown physical parameters are available as prior information. The system function is modeled by a GP with the mean function given by the one-step integration of ODEs. By this way, all dimensions of the system function are correlated because they share the same unknown physical parameters. This correlation allows the flow of information between multiple dimensions of the system function which in turn will lead to an improved model. With the existing models as baselines, we show the benefits of the proposed model by simulations.
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Index Terms
- Learning dynamical systems using a novel multiple-output Gaussian process model
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