A Sampling Complexity-aware Framework for Discrete-time Fractional-Order Dynamical System Identification
The paper proposes an affine discrete-time fractional-order dynamical system (FoDS) identification algorithm that handles noisy data by reformulating the problem as a least squares estimation. It derives rigorous sample complexity bounds showing that the required number of samples scales linearly with the state dimension and inversely with desired accuracy, providing a robust framework for parameter inference.