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Marvin L. System Identification with MATLAB. Non Linear Models

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Marvin L. System Identification with MATLAB. Non Linear Models
Marvin L. System Identification with MATLAB. Non Linear Models, ODEs and Time Series. - 2016. - 207p.
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 Identification
Nonlinear Model Identification
Fit Nonlinear Models
Identifying Nonlinear ARX Models
Nonlinearity Estimators for Nonlinear ARX Models
Estimate Nonlinear ARX Models in the GUI
Estimate Nonlinear ARX Models at the Command Line
Validating Nonlinear ARX Models
Identifying Hammerstein-Wiener Models
Nonlinearity Estimators for Hammerstein-Wiener Models
Estimation Algorithm for Hammerstein-Wiener Models
Validating Hammerstein-Wiener Models
Linear Approximation of Nonlinear Black-Box Models
ODE Parameter Estimation (Grey-Box Modeling)
Estimating Linear Grey-Box Models
Estimating Nonlinear Grey-Box Models
After Estimating Grey-Box Models
Estimating Coefficients of ODEs to Fit Given Solution
Estimate Model Using Zero/Pole/Gain Parameters
Time Series Identification
Estimating Time-Series Power Spectra
Estimate Time-Series Power Spectra Using the GUI
Estimate Time-Series Power Spectra at the Command Line
Estimating AR and ARMA Models
Estimating Polynomial Time-Series Models in the GUI
Estimating AR and ARMA Models at the Command Line
Estimating State-Space Time-Series Models
Estimating State-Space Models at the Command Line
Identify Time-Series Models at Command Line
Estimating Nonlinear Models for Time-Series Data
Estimating ARIMA Models
Analyzing of Time-Series Models
Recursive Model Identification
General Form of Recursive Estimation Algorithm
Kalman Filter Algorithm
Recursive Estimation and Data Segmentation
Techniques in System Identification Toolbox
Model Analysis
Validating Models After Estimation
Plotting Models in the GUI
Simulating and Predicting Model Output
Simulation and Prediction in the GUI
Simulation and Prediction at the Command Line
Predict Using Time-Series Model
Residual Analysis
Impulse and Step Response Plots
Frequency Response Plots
Displaying the Confidence Interval
Noise Spectrum Plots
Pole and Zero Plots
Analyzing MIMO Models
Akaike’s Criteria for Model Validation
Troubleshooting Models
Unstable Models
Missing Input Variables
Complicated Nonlinearities
Spectrum Estimation Using Complex Data
System Identification Toolbox Blocks
Using System Identification Toolbox Blocks in Simulink Models
Identifying Linear Models
Simulating Identified Model Output in Simulink
Simulate Identified Model Using Simulink Software
System Identification Tool GUI
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