Publisher's Synopsis
System Identification Toolbox provides MATLAB functions, Simulink blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use 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 models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation. The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent 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 system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting. The most important content that this book provides are the following: -System Identification Overview -What Is System Identification? -About Dynamic Systems and Models -System Identification Requires Measured Data -Building Models from Data -Black-Box Modeling -Grey-Box Modeling -Evaluating Model Quality -When to Use the App vs. the Command Line -System Identification Workflow -Commands for Model Estimation -Linear Model Identification -Identify Linear Models Using System Identification App -Preparing Data for System Identification -Saving the Session -Estimating Linear Models Using Quick Start -Estimating Linear Models -Viewing Model Parameters -Exporting the Model to the MATLAB Workspace -Exporting the Model to the Linear System Analyzer -Identify Linear Models Using the Command Line -Preparing Data -Estimating Impulse Response Models -Estimating Delays in the Multiple-Input System -Estimating Model Orders Using an ARX Model Structure -Estimating Transfer Functions -Estimating Process Models -Estimating Black-Box Polynomial Models -Simulating and Predicting Model Output -Identify Low-Order Transfer Functions (Process Models) -Using System Identification App -What Is a Continuous-Time Process Model? -Preparing Data for System Identification -Estimating a Second-Order Transfer Function (Process Model) -with Complex Poles -Estimating a Process Model with a Noise Component -Viewing Model Parameters -Exporting the Model to the MATLAB Workspace -Simulating a System Identification Toolbox Model in Simulink Software -Estimating Models Using Frequency-Domain Data -Advantages of Using Frequency-Domain Data -Representing Frequency-Domain Data in the Toolbox -Preprocessing Frequency-Domain Data for Model -Estimation -Estimating Linear Parametric Models -Validating Estimated Model -Next Steps After Identifying a Model -Nonlinear Model Identification -Identify Nonlinear Black-Box Models Using System -Identification App -What Are Nonlinear Black-Box Models? -Preparing Data -Estimating Nonlinear ARX Models -Estimating Hammerstein-Wiener Models