Autoregressive spectral modeling: Difficulties and remedies

Marc Mignolet, Pol D. Spanos

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Many problems of non-linear mechanics can be treated only by the Monte Carlo method. An efficient approach for conducting Monte Carlo simulations relies on using digital filters. In this paper the difficulties associated with the computation of reliable digital autoregressive (AR) approximations of stochastic processes of engineering practice with troublesome spectra, such as the Pierson-Moskowitz sea waves spectrum and the Davenport wind spectrum, are investigated from a new perspective. It is known that the AR model whose coefficients are obtained as straightforward applications of the AR approximation technique can exhibit large spectral fluctuations. This symptom is explained by considering the mathematical peculiarities of the target spectra. Further, mitigation techniques that lead to reliable AR approximations are presented. Finally, a measure of the suitability of a given spectrum for AR modeling is presented. This measure can be used to predict the quality of a particular AR approximation or to select an appropriate AR system order.

Original languageEnglish (US)
Pages (from-to)911-930
Number of pages20
JournalInternational Journal of Non-Linear Mechanics
Volume26
Issue number6
DOIs
StatePublished - 1991

Fingerprint

Digital filters
Random processes
Mechanics
Monte Carlo methods
Modeling
approximation
Approximation
digital filters
stochastic processes
Digital Filter
Monte Carlo method
Autoregressive Model
Monte Carlo simulation
Stochastic Processes
engineering
conduction
Monte Carlo Simulation
Fluctuations
Engineering
Predict

ASJC Scopus subject areas

  • Mechanical Engineering
  • Statistical and Nonlinear Physics

Cite this

Autoregressive spectral modeling : Difficulties and remedies. / Mignolet, Marc; Spanos, Pol D.

In: International Journal of Non-Linear Mechanics, Vol. 26, No. 6, 1991, p. 911-930.

Research output: Contribution to journalArticle

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