Response of Systems Under Non-Gaussian Random Excitations |
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Authors: | G Q Cai Y Suzuki |
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Institution: | (1) Center for Applied Stochastics Research, Florida Atlantic University, Boca Raton, FL, 33431, U.S.A.;(2) Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japan |
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Abstract: | The approach of nonlinear filter is applied to model non-Gaussian stochastic processes defined in an infinite space, a semi-infinite
space or a bounded space with one-peak or multiple peaks in their spectral densities. Exact statistical moments of any order
are obtained for responses of linear systems jected to such non-Gaussian excitations. For nonlinear systems, an improved linearization
procedure is proposed by using the exact statistical moments obtained for the responses of the equivalent linear systems,
thus, avoiding the Gaussian assumption used in the conventional linearization. Numerical examples show that the proposed procedure
has much higher accuracy than the conventional linearization in cases of strong system nonlinearity and/or high excitation
non-Gaussianity.
An erratum to this article is available at . |
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Keywords: | linear and nonlinear systems Monte Carlo simulation non-Gaussian excitations nonlinear filter random vibration statistical linearization stochastic differential equations |
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