A direct determination of ARMA algorithms for the simulation of stationary random processes

Marc Mignolet; Pol D. Spanos

doi:10.1016/0020-7462(90)90018-5

A direct determination of ARMA algorithms for the simulation of stationary random processes

Marc Mignolet, Pol D. Spanos

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

An efficient technique to determine autoregressive moving average (ARMA) algorithms for simulating realizations of multivariate random processes with a specified (target) spectral matrix is presented. The computation of the parameters of the ARMA model is accomplished by relying on the minimization of frequency domain errors. The need to perform a spectral factorization is discussed and some new algorithms to perform this operation are presented. Finally, the features of this technique are critically assessed and examples of application are given in context with nonlinear mechanics problems.

Original language	English (US)
Pages (from-to)	555-568
Number of pages	14
Journal	International Journal of Non-Linear Mechanics
Volume	25
Issue number	5
DOIs	https://doi.org/10.1016/0020-7462(90)90018-5
State	Published - 1990

ASJC Scopus subject areas

Mechanics of Materials
Mechanical Engineering
Applied Mathematics

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abstract = "An efficient technique to determine autoregressive moving average (ARMA) algorithms for simulating realizations of multivariate random processes with a specified (target) spectral matrix is presented. The computation of the parameters of the ARMA model is accomplished by relying on the minimization of frequency domain errors. The need to perform a spectral factorization is discussed and some new algorithms to perform this operation are presented. Finally, the features of this technique are critically assessed and examples of application are given in context with nonlinear mechanics problems.",

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AB - An efficient technique to determine autoregressive moving average (ARMA) algorithms for simulating realizations of multivariate random processes with a specified (target) spectral matrix is presented. The computation of the parameters of the ARMA model is accomplished by relying on the minimization of frequency domain errors. The need to perform a spectral factorization is discussed and some new algorithms to perform this operation are presented. Finally, the features of this technique are critically assessed and examples of application are given in context with nonlinear mechanics problems.

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A direct determination of ARMA algorithms for the simulation of stationary random processes

Abstract

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