Nonlinear set membership regression with adaptive hyper-parameter estimation for online learning and control
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...
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| Format: | Conference item |
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IEEE
2018
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