Stochastic Signals and Systems


Code: ΜΕΑ_ΚΕ26


Introduction to stochastic signals and systems with emphasis on modeling, analysis, prediction, estimation and control. The main focus is on mechanical systems and random vibration. Contents include: Definitions and importance of stochastic signals and systems. Stochastic signals in the time domain and the autocovariance function. Stationarity and ergodicity. Stochastic signals in the frequency domain and the power spectral density. Linear stationary stochastic signals and the ARMA (Autoregressive Moving Average) representation. Model based signal analysis. Poles and zeros. Relationship between discrete-time and continuous-time models. Homogeneously non-stationary signals and the Integrated ARMA representation. Seasonal ARMA models. Prediction of stochastic signals. Estimation and identification of stochastic signals from signal measurements. Extensions of ARMA models, models with exogenous excitation and stochastic control. Student project using Matlab.


Fassois Spilios