화학공학소재연구정보센터
Journal of Process Control, Vol.20, No.1, 195-205, 2010
Variance decompositions of nonlinear-dynamic stochastic systems
A variance decomposition approach to quantify the effects of stochastic variables in nonlinear-dynamic models is developed The decomposition is taken temporally with respect to the Source of disturbance The methodology uses Monte-Carlo methods to estimate the variance decomposition using the ANOVA-like procedures proposed in [G.E.B. Archer. A Saltelli. I M Sobol, Sensitivity measures, anova-like techniques and the use of bootstrap, Journal of Statistical Computation and Simulation 58 (1997) 99-120. A Saltelli. S Tarantola. K. Chan. A quantitative model-independent method for global sensitivity analysis of model Output. Technometrics 41 (1999) 39-56] The results in this paper generalize file variance decomposition results that are obtained analytically for linear systems. (C) 2009 Elsevier Ltd. All rights reserved.