System Identification Toolbox
System Identification Toolbox offers MATLAB functions, Simulink blocks, and an app for modeling, time-series analysis, and forecasting. Estimate linear and nonlinear models from time/frequency data using techniques like AR, ARMA, nonlinear ARX, and Hammerstein-Wiener with ML methods (GP, SVM). Build neural ODEs for nonlinear dynamics and perform grey-box modeling. Integrate models in Simulink for control, diagnostics, and prognostics. Enable online estimation with Kalman and particle filters, and generate C/C++ code for embedded deployment.
Linear System Identification
Estimate linear models from time or frequency data for control, simulation, and forecasting. Create transfer functions, process, state-space, or polynomial models in continuous/discrete time. Use spectral analysis for frequency response and assess uncertainty effects on model response.
System Identification App
Use the System Identification app to interactively estimate, compare, and validate linear and nonlinear models. Import and preprocess time/frequency-domain data and analyze model properties.
Nonlinear System Identification
Estimate nonlinear ARX and Hammerstein-Wiener models to capture system dynamics. Use wavelet networks, tree-partitioning, or sigmoid networks for nonlinearities. Specify or auto-select regressors in nonlinear ARX; use Hammerstein-Wiener to model input/output nonlinearities in linear systems.
AI-Based Nonlinear System Identification
Combine machine learning and deep learning with nonlinear ARX and Hammerstein-Wiener models to capture system dynamics. Use SVMs, tree ensembles, Gaussian processes, and neural networks, or build nonlinear state-space models with neural ODEs.
Grey-Box System Identification
Model your system with linear/nonlinear equations or state-space representations, and estimate grey-box parameters from input-output data to capture system dynamics.
Time-Series Models
Fit time-series or signal models to measured data and forecast using linear (AR, ARMA, ARIMA, state-space) or nonlinear (nonlinear ARX) models.
Online Estimation
Estimate system models in real-time with recursive algorithms that adapt to new data. Use Kalman or particle filters for state estimation.
Control System Design and Simulink
Use estimated models as plant models for controller design with Control System Toolbox. Implement them in Simulink using built-in blocks for analysis, virtual sensors, ROM, and control design.
Deployment
Generate C/C++ or Structured Text code for estimated models and state estimators using Simulink Coder, Simulink PLC Coder, or MATLAB Coder. Use for online fault detection, ROM, and diagnostics. MATLAB Compiler enables standalone app creation.