Gaussian basis function fitting for stationary solutions of a double-flag hysteretic nonlinear oscillator under colored noise excitation

The double-flag hysteretic nonlinear model is widely used in industrial applications, particularly for describing the stress-strain constitutive relationship of shape memory alloys (SMAs). This study investigates the vibrational response of a double-flag hysteretic nonlinear oscillator under Gaussian colored noise excitation. A semi-analytical method, Gaussian basis function (GBF) fitting, is employed to approximate the system’s steady-state probability density function (PDF) as a weighted sum of Gaussian basis functions. A loss function is constructed using stochastic sampling, and optimal weight coefficients are determined through minimization. The method yields the steady-state joint PDF of displacement and velocity, as well as their marginal PDFs. The results show good agreement with Monte Carlo simulation (MCS) data.

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Publication Details

Journal
Journal of Vibroengineering
Published
2026-09-21
DOI
https://doi.org/10.21595/jve.2026.26486
Primary Topic
Shape Memory Alloy Transformations
Type
article
Field-Weighted Citation Impact
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article

Gaussian basis function fitting for stationary solutions of a double-flag hysteretic nonlinear oscillator under colored noise excitation

Gen Ge, Wenhan Li, Yanfan Bo, Jianguo Tan
Journal of Vibroengineering
Shape Memory Alloy Transformations
article

Gaussian basis function fitting for stationary solutions of a double-flag hysteretic nonlinear oscillator under colored noise excitation

Gen Ge, Wenhan Li, Yanfan Bo, Jianguo Tan
article en

Abstract

The double-flag hysteretic nonlinear model is widely used in industrial applications, particularly for describing the stress-strain constitutive relationship of shape memory alloys (SMAs). This study investigates the vibrational response of a double-flag hysteretic nonlinear oscillator under Gaussian colored noise excitation. A semi-analytical method, Gaussian basis function (GBF) fitting, is employed to approximate the system’s steady-state probability density function (PDF) as a weighted sum of Gaussian basis functions. A loss function is constructed using stochastic sampling, and optimal weight coefficients are determined through minimization. The method yields the steady-state joint PDF of displacement and velocity, as well as their marginal PDFs. The results show good agreement with Monte Carlo simulation (MCS) data.

Journal of Vibroengineering
Openalex Percentile: Top 25%
Shape Memory Alloy Transformations
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