Linear models for non-Gaussian processes and applications to linear random vibration

Abstract

Linear models are finite sums of specified deterministic, continuous functions of time with random coefficients. It is shown that linear models provide ( i ) accurate approximations for real-valued non-Gaussian processes with continuous samples defined on bounded time intervals, ( i i ) simple solutions for linear random vibration problems with non-Gaussian input, and ( i i i ) efficient techniques for selecting optimal designs from collections of proposed alternatives. Theoretical arguments and numerical examples are presented to establish properties of linear models, illustrate the construction of linear models, solve linear random vibration with non-Gaussian input, and propose an approach for optimal design of linear dynamic systems. It is shown that the proposed linear model provides an efficient tool for analyzing linear systems in non-Gaussian environment.