Basic System Identification with Matlab by T. Kendall-P2P
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.

Basic System Identification with Matlab by T. Kendall-P2P
English | 26 Oct 2016 | ASIN: B01M9GAUTN | 174 Pages | PDF | 3,42 MB
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