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An algorithm to simulate nonstationary and non-Gaussian stochastic processes.

H P Hong1, X Z Cui1, D Qiao2

  • 1Department of Civil and Environmental Engineering, University of Western Ontario, London, N6A 5B9 Canada.

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|November 22, 2021
PubMed
Summary
This summary is machine-generated.

We developed a new iterative power and amplitude correction (IPAC) algorithm to simulate complex, nonstationary, and non-Gaussian processes. This versatile method enhances simulations by defining stochastic processes in various transform domains.

Keywords:
Continuous wavelet transformsNonstationary and non-Gaussian processS-transformSeismic ground motionsSimulationWind velocity

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Area of Science:

  • Signal processing
  • Stochastic processes
  • Computational physics

Background:

  • Simulating nonstationary and non-Gaussian processes is challenging.
  • Existing methods have limitations in handling complex stochastic behaviors.

Purpose of the Study:

  • To introduce a novel iterative power and amplitude correction (IPAC) algorithm.
  • To enable the simulation of nonstationary and non-Gaussian stochastic processes.

Main Methods:

  • The IPAC algorithm operates in the transform domain.
  • It extends existing algorithms like iterative amplitude adjusted Fourier transform and spectral correction.
  • Supports various transforms including Fourier, S-transform, and continuous wavelet transforms.

Main Results:

  • The IPAC algorithm successfully simulates target marginal probability distribution functions and power spectral densities.
  • Demonstrates versatility across different transform domains.
  • Numerical examples confirm the algorithm's efficiency and applicability.

Conclusions:

  • The IPAC algorithm provides a robust and efficient method for simulating complex stochastic processes.
  • Its adaptability to various transforms makes it a valuable tool in signal processing and related fields.