This chapter presents the modeling of linear dynamic systems excited by random inputs, called noise. Continuous‐time systems and discrete‐time systems are discussed. The state‐space models for continuous time and discrete time are presented, and it is shown how the latter can be derived from the former by discretization. The state‐space model directly defined in discrete time is also discussed. The Markov property of the state of a linear system driven by white noise is discussed and used to obtain the propagation equations for the mean and covariance of the state. The power spectral density (the Fourier transform of the autocorrelation function) of the output of a linear system is related to the state space representation via the transfer function, and it is shown how its factorization makes it possible to prewhiten an autocorrelated random process or sequence. A problem solving section appears at the end of the chapter.
Linear Dynamic Systems with Random Inputs
2002-01-04
20 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Dynamic Response of Tractor-Semitrailer Vehicle to Random Inputs
Taylor & Francis Verlag | 1977
|Dynamic Response of a Six-axle Locomotive to Random Track Inputs
Kraftfahrwesen | 1980
|Dynamic Response of a Six-axle Locomotive to Random Track Inputs
Taylor & Francis Verlag | 1980
|Observer Design for Linear Descriptor Systems with Unknown Inputs
British Library Online Contents | 1995
|