SYSTEM IDENTIFICATION with MATLAB. Non Linear Models, ODEs and Time Series

In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can...
More Description

In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. Thisb book develops de next task with models:Nonlinear Black-Box Model IdentificationNonlinear Model IdentificationFit Nonlinear ModelsIdentifying Nonlinear ARX ModelsNonlinearity Estimators for Nonlinear ARX ModelsEstimate Nonlinear ARX Models in the GUIEstimate Nonlinear ARX Models at the Command LineValidating Nonlinear ARX ModelsIdentifying Hammerstein-Wiener ModelsNonlinearity Estimators for Hammerstein-Wiener ModelsEstimation Algorithm for Hammerstein-Wiener ModelsValidating Hammerstein-Wiener ModelsLinear Approximation of Nonlinear Black-Box ModelsODE Parameter Estimation (Grey-Box Modeling)Estimating Linear Grey-Box ModelsEstimating Nonlinear Grey-Box ModelsAfter Estimating Grey-Box ModelsEstimating Coefficients of ODEs to Fit Given SolutionEstimate Model Using Zero/Pole/Gain ParametersTime Series IdentificationEstimating Time-Series Power SpectraEstimate Time-Series Power Spectra Using the GUIEstimate Time-Series Power Spectra at the Command LineEstimating AR and ARMA ModelsEstimating Polynomial Time-Series Models in the GUIEstimating AR and ARMA Models at the Command LineEstimating State-Space Time-Series ModelsEstimating State-Space Models at the Command LineIdentify Time-Series Models at Command LineEstimating Nonlinear Models for Time-Series DataEstimating ARIMA Models Analyzing of Time-Series ModelsRecursive Model IdentificationGeneral Form of Recursive Estimation AlgorithmKalman Filter AlgorithmRecursive Estimation and Data SegmentationTechniques in System Identification ToolboxModel AnalysisValidating Models After EstimationPlotting Models in the GUISimulating and Predicting Model OutputSimulation and Prediction in the GUISimulation and Prediction at the Command LinePredict Using Time-Series ModelResidual AnalysisImpulse and Step Response PlotsFrequency Response PlotsDisplaying the Confidence IntervalNoise Spectrum PlotsPole and Zero PlotsAnalyzing MIMO ModelsAkaike's Criteria for Model ValidationTroubleshooting ModelsUnstable ModelsMissing Input VariablesComplicated NonlinearitiesSpectrum Estimation Using Complex Data System Identification Toolbox BlocksUsing System Identification Toolbox Blocks in Simulink ModelsIdentifying Linear ModelsSimulating Identified Model Output in SimulinkSimulate Identified Model Using Simulink SoftwareSystem Identification Tool GUI