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create linear and nonlinear dynamic system models from input-output data

release notes

release notes

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.

get started

learn the basics of system identification toolbox

plot, analyze, detrend, and filter time- and frequency-domain data, generate and import data

linear model identification

identify impulse-response, frequency-response, and parametric models, such as linear state-space and transfer function models

nonlinear model identification

identify nonlinear arx, hammerstein-wiener, and grey-box models

grey-box model estimation

estimate coefficients of linear and nonlinear differential, difference and state-space equations

compare model to measured output, residual analysis, response plots with confidence bounds

discretize models, convert models to other types, linearize nonlinear models, simulate and predict output

time series analysis

analyze time series data by identifying linear and nonlinear models such as ar, arma, state-space, and grey-box models, performing spectral analysis, and forecasting model outputs

online estimation

estimate model parameters and states during system operation, generate code and deploy to embedded targets