A Gaussian Process model learns impact-state-dependent bounce parameters for an impulse contact model across 10 diverse racket rubbers, reducing velocity and spin prediction errors versus constant baselines and supporting online adaptation.
Deep networks for system identification: a survey
2 Pith papers cite this work. Polarity classification is still indexing.
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xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.
citing papers explorer
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Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis
A Gaussian Process model learns impact-state-dependent bounce parameters for an impulse contact model across 10 diverse racket rubbers, reducing velocity and spin prediction errors versus constant baselines and supporting online adaptation.
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xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.