A Sampling Complexity-aware Framework for Discrete-time Fractional-Order Dynamical System Identification

Abstract:A variety of complex biological, natural and man-made systems exhibit non-Markovian dynamics that can be modeled through fractional order differential equations, yet, we lack sample comlexity aware system identification strategies. Towards this end, we propose an affine discrete-time fractional order dynamical system (FoDS) identification algorithm and provide a detailed sample complexity analysis. The algorithm effectively addresses the challenges of FoDS identification in the presence of noisy data. The proposed algorithm consists of two key steps. Firstly, it avoids solving higher-order polynomial equations, which would otherwise result in multiple potential solutions for the fractional orders. Secondly, the identification problem is reformulated as a least squares estimation, allowing us to infer the system parameters. We derive the expectation and probabilistic bounds for the FoDS parameter estimation error, assuming prior knowledge of the functions \( f \) and \( g \) in the FoDS model. The error decays at a rate of \( N = O\left( \frac{d}{\epsilon} \right) \), where \( N \) is the number of samples, \( d \) is the dimension of the state variable, and \( \epsilon \) represents the desired estimation accuracy. Simulation results demonstrate that our theoretical bounds are tight, validating the accuracy and robustness of this algorithm.

Systems and Control (eess.SY); Machine Learning (cs.LG)

arXiv:2501.17499 [eess.SY]

(or arXiv:2501.17499v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2501.17499 Focus to learn more arXiv-issued DOI via DataCite

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