Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control*

@article{Calliess2018NonlinearSM,
  title={Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control*},
  author={Jan-P. Calliess and Stephen J. Roberts and Carl E. Rasmussen and Jan M. Maciejowski},
  journal={2018 European Control Conference (ECC)},
  year={2018},
  pages={1-6}
}
Methods known as Lipschitz Interpolation or Nonlinear Set Membership regression have become established tools for nonparametric system-identification and data-based control. They utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Unfortunately, they rely on the a priori knowledge of a Lipschitz constant of the underlying target function which serves as a hyper-parameter. We propose a closed-form estimator of the Lipschitz constant that is robust to… CONTINUE READING

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