Upper generalization bounds for neural oscillators scale polynomially with MLP size and time length, avoiding the curse of parametric complexity, with numerical validation on a Bouc-Wen nonlinear system.
year 2024
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Upper bounds are derived showing that neural oscillator approximation errors for causal operators and stable second-order dynamical systems scale polynomially with the reciprocals of the widths of the two MLPs.
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Upper Generalization Bounds for Neural Oscillators
Upper generalization bounds for neural oscillators scale polynomially with MLP size and time length, avoiding the curse of parametric complexity, with numerical validation on a Bouc-Wen nonlinear system.
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Upper Approximation Bounds for Neural Oscillators
Upper bounds are derived showing that neural oscillator approximation errors for causal operators and stable second-order dynamical systems scale polynomially with the reciprocals of the widths of the two MLPs.