EconPapers    
Economics at your fingertips  
 

Statistical performance analysis of nonlinear stochastic systems by the Monte Carlo method

James H. Taylor

Mathematics and Computers in Simulation (MATCOM), 1981, vol. 23, issue 1, 21-33

Abstract: Until the recent advent of extended covariance analysis utilizing quasi-linearization techniques, the only approach for assessing the performance of a nonlinear system with random inputs and initial conditions has been the monte carlo method. This method involves direct simulation, i.e., determining the system response to a finite number of “typical” initial conditions and noise input functions which are generated according to their specified statistics, and averaging over the resulting ensemble of responses (“trials”) to obtain estimated or sample statistics.

Date: 1981
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0378475481900045
Full text for ScienceDirect subscribers only

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:eee:matcom:v:23:y:1981:i:1:p:21-33

DOI: 10.1016/0378-4754(81)90004-5

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:matcom:v:23:y:1981:i:1:p:21-33