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The PV-NN parameter identifier for dynamical system driven by fractional Brownian motion

Author

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  • Hou, Wentao
  • Ma, Shaojuan

Abstract

This paper proposes a combined identifier based on the power variation (PV) statistic and neural network (NN), capable of jointly identifying all parameters of a dynamical system driven by fractional Brownian motion (FBM) in discrete sample trajectories. Firstly, the PV statistic and NN are integrated to construct the new combined identifier. Then, the FBM is modeled as a random effect in the dynamical system, and the proposed PV-NN identifier is applied to identify all system parameters. Finally, using both linear and nonlinear systems as examples, a series of numerical simulations are conducted under 11 different Hurst exponent values. The effectiveness of the proposed identifier is verified through experiments designed using the OA9(34) orthogonal table. Additionally, the paper also discusses the identification performance of the PV-NN identifier. The results show that, compared to the parameter estimation neural network and maximum likelihood identifiers, the proposed method identifies all parameters in the system more quickly and accurately.

Suggested Citation

  • Hou, Wentao & Ma, Shaojuan, 2026. "The PV-NN parameter identifier for dynamical system driven by fractional Brownian motion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 249(C), pages 910-934.
    • Handle: RePEc:eee:matcom:v:249:y:2026:i:c:p:910-934
    DOI: 10.1016/j.matcom.2026.06.008
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