Basic SYSTEM IDENTIFICATION with MATLAB

System Identification Toolbox constructs mathematical models of dynamic systems from measured input-output data. It provides MATLAB® functions, Simulink blocks, and an interactive tool for creating and using models of dynamic systems not easily modeled from first principles or specifications You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process odels, and state-space models. The toolbox provides maximum likelihood, prediction-error minimization (PEM), subspace system identification, and other identification techniques.
For nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for prediction of system response and for simulation in Simulink. The toolbox also lets you model time-series data and perform time-series forecasting. The more important content in this book is the next:

• Transfer function, process model, and state-space model identification using time-domain and frequency-domain response data
• Autoregressive (ARX, ARMAX), Box-Jenkins, and Output-Error model estimation using maximum likelihood, prediction-error minimization
(PEM), and subspace system identification techniques • Time-series modeling (AR, ARMA, ARIMA) and forecasting
• Identification of nonlinear ARX models and Hammerstein-Weiner models with input-output nonlinearities such as saturation and dead zone
• Linear and nonlinear grey-box system identification for estimation of user-defined models
• Delay estimation, detrending, filtering, resampling, and reconstruction of missing data