Generation of Two Correlated Stationary Gaussian Processes

Guo-Qiang Cai, Ronghua Huan and Weiqiu Zhu
Additional contact information
Guo-Qiang Cai: Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
Ronghua Huan: Department of Mechanics, State Key Laboratory of Fluid Power and Mechatronic and Control, Zhejiang University, Hangzhou 310027, China
Weiqiu Zhu: Department of Mechanics, State Key Laboratory of Fluid Power and Mechatronic and Control, Zhejiang University, Hangzhou 310027, China

Mathematics, 2021, vol. 9, issue 21, 1-12

Abstract: Since correlated stochastic processes are often presented in practical problems, feasible methods to model and generate correlated processes appropriately are needed for analysis and simulation. The present paper systematically presents three methods to generate two correlated stationary Gaussian processes. They are (1) the method of linear filters, (2) the method of series expansion with random amplitudes, and (3) the method of series expansion with random phases. All three methods intend to match the power spectral density for each process but use information of different levels of correlation. The advantages and disadvantages of each method are discussed.

Keywords: correlated stochastic processes; liner filters; series expansion; random amplitudes; random phases; simulations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/21/2687/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/21/2687/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:21:p:2687-:d:662691

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().