Asymptotic regularity of a family of measures corresponding to a Gaussian random process which contains a white noise component for a parametric family of spectral densities

1984 ◽  
Vol 25 (3) ◽  
pp. 1165-1181
Author(s):  
Yu. I. Ingster
Keyword(s):  
White Noise ◽  
Random Process ◽  
Parametric Family ◽  
Asymptotic Regularity ◽  
Spectral Densities ◽  
Gaussian Random Process ◽  
Noise Component

scholarly journals On Spectrum Estimation of a Clipped Gaussian Random Process

1972 ◽  
Vol 12 (2) ◽  
pp. 11-15
Author(s):  
V. G. Alekseyev
Keyword(s):  
Random Process ◽  
Spectrum Estimation ◽  
Gaussian Random Process

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: В. Алексеев. Об оценке спектра квантованного по уровню гауссовского случайного процесса V. Aleksejevas. Apie atsitiktinio Gauso proceso spektro, kvantuoto pagal lygmenį, įvertinimą H

A Mathematical Basis for the Random Decrement Vibration Signature Analysis Technique

1982 ◽  
Vol 104 (2) ◽  
pp. 307-313 ◽  
Author(s):  
J. K. Vandiver ◽  
A. B. Dunwoody ◽  
R. B. Campbell ◽  
M. F. Cook
Keyword(s):  
Random Process ◽  
Linear Time ◽  
Signature Analysis ◽  
Gaussian Random Process ◽  
Mathematical Basis ◽  
Linear Time Invariant ◽  
Random Decrement Technique ◽  
Random Decrement ◽  
Vibration Signature ◽  
Linear Time Invariant System

The mathematical basis for the Random Decrement Technique of vibration signature analysis is established. The general relationship between the autocorrelation function of a random process and the Randomdec signature is derived. For the particular case of a linear time invariant system excited by a zero-mean, stationary, Gaussian random process, a Randomdec signature of the output is shown to be proportional to the auto-correlation of the output. Example Randomdec signatures are computed from acceleration response time histories from an offshore platform.

Identification of Non-Gaussian Stochastic System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Sung-man Park ◽  
O-shin Kwon ◽  
Jin-sung Kim ◽  
Jong-bok Lee ◽  
Hoon Heo
Keyword(s):  
Random Process ◽  
Random Noise ◽  
Stochastic Simulations ◽  
Kolmogorov Equation ◽  
Gaussian Random Process ◽  
System Parameters ◽  
Analysis Technique ◽  
Gaussian Analysis ◽  
Gaussian System ◽  
Non Gaussian

This paper proposes a method to identify non-Gaussian random noise in an unknown system through the use of a modified system identification (ID) technique in the stochastic domain, which is based on a recently developed Gaussian system ID. The non-Gaussian random process is approximated via an equivalent Gaussian approach. A modified Fokker–Planck–Kolmogorov equation based on a non-Gaussian analysis technique is adopted to utilize an effective Gaussian random process that represents an implied non-Gaussian random process. When a system under non-Gaussian random noise reveals stationary moment output, the system parameters can be extracted via symbolic computation. Monte Carlo stochastic simulations are conducted to reveal some approximate results, which are close to the actual values of the system parameters.

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