System Identification Toolbox™ provides MATLAB^® functions, Simulink^® blocks, and an app for dynamic system modeling, time-series analysis, and forecasting. You can learn dynamic relationships among measured variables to create transfer functions, process models, and state-space models in either continuous or discrete time while using time- or frequency-domain data. You can forecast time series using AR, ARMA, and other linear and nonlinear autoregressive modeling techniques.

The toolbox lets you estimate nonlinear system dynamics using Hammerstein-Wiener and Nonlinear ARX models with machine learning techniques such as Gaussian Processes (GP), Support Vector Machines (SVM), and other representations. Alternatively, you can create neural ordinary differential equation (ODE) models using deep learning to capture nonlinear system dynamics. The toolbox lets you perform grey-box system identification for estimating parameters of a user-defined model. You can integrate identified models into Simulink for rapid simulations to enable control design and diagnostic and prognostic applications.

You can perform online parameter and state estimation using extended or unscented Kalman filters and particle filters for adaptive control, fault detection, and soft sensing applications. The toolbox lets you generate C/C++ code for online estimation algorithms to target embedded devices.