MATLAB Examples

The effect of intersample behavior on the estimation of continuous-time models using discrete-time frequency-response data.

Convert a frequency-response data (FRD) model to a transfer function model. You treat the FRD model as estimation data and then estimate the transfer function.

Create a periodic random Gaussian input signal using idinput.

Create a multi-experiment, time-domain data set by merging only the accurate data segments and ignoring the rest.

Deal with multiple experiments and merging models when working with System Identification Toolbox™ for estimating and refining models.

Generate output data by simulating a model using an input signal created using idinput .

Create a multiexperiment iddata object by merging iddata objects, where each contains data from a single experiment or is a multiexperiment data set.

Estimate the unknown parameters of a continuous-time model.

Access the estimation report.

Estimate model parameters using linear and nonlinear grey-box modeling.

Represent the structure of the following continuous-time model:

Estimate parameters in user-defined model structures. Such structures are specified by IDGREY (linear state-space) or IDNLGREY (nonlinear state-space) models. We shall investigate

Estimate a transfer function from frequency response data. You use Simulink® Control Design™ to collect frequency response data from a Simulink model and the tfest command to estimate a

Illustrates how models simulated in Simulink® can be identified using System Identification Toolbox™. The example describes how to deal with continuous-time systems and delays, as well

Develop and analyze simple models from a real laboratory process data. We start with a small description of the process, learn how to import the data to the toolbox and preprocess/condition

Manage data and model objects available in the System Identification Toolbox™. System identification is about building models from data. A data set is characterized by several pieces of

Obtain linear approximations of a complex, nonlinear system by means of linear model identification. The approach is based on selection of an input signal that excites the system. A linear

The benefits of regularization for identification of linear and nonlinear models.

Estimate models using frequency domain data. The estimation and validation of models using frequency domain data work the same way as they do with time domain data. This provides a great

Estimate a transfer function from measured signal data.

Build simple process models using System Identification Toolbox™. Techniques for creating these models and estimating their parameters using experimental data is described. This

Perform and troubleshoot the identification of a SISO system using frequency-response data (FRD). The techniques explained here can also be applied to MIMO models and frequency-domain

Estimate ARMAX and OE-form models using the state-space estimation approach.

Several identification methods available in System Identification Toolbox™. We begin by simulating experimental data and use several estimation techniques to estimate models from the

Estimate the unknown parameters of a discrete-time model.

Identify a transfer function containing a specified number of poles for given data.

Estimate a process model with fixed parameters.

Estimate a transfer function model with unknown transport delays and apply an upper bound on the unknown transport delays.

Identify a transfer function to fit a given frequency response data (FRD) containing additional phase roll off induced by input delay.

Estimate transfer function models with I/O delays.

Forecast time series data from a system using an ARMA model. Load the time series data that is to be forecasted.

Manually reproduce forecasting results that are obtained using the forecast command. You first use the forecast command to forecast time series data into the future. You then compare the

Perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario. The predator-prey population-change dynamics are modeled

Validate an estimated model by comparing the simulated model output with measured data that was not used for the original estimation.

Create input data and a model, and then use the data and the model to simulate output data.

Simulate a continuous-time state-space model using a random binary input u and a sample time of 0.1 s .

Refine models for which you have initial parameter guesses.

Estimate an initial model and refine it using pem .

Estimate the initial conditions for a simulation segment of an identified model by using the end states of the previous simulation segment.

In this example, you first use the compare command to match the simulated output of a nonlinear ARX model to measured data. You then reproduce the simulated output using a Nonlinear ARX Model

Visualize both the predicted model response and the simulated model response of an identified linear model.

Identify models with prediction focus and with simulation focus. Compare the responses of prediction-focus and simulation-focus models against the original estimation data, and

Set the initial states for simulating a linear model such that the simulation provides a best fit to measured input-output data.

Compare the simulated output of a Hammerstein-Wiener Model block to the measured output of a system. You improve the agreement between the measured and simulated responses by estimating

How NRMSE fit values computed by model identification functions and by the compare function can differ because of differences in initial conditions and prediction horizon settings.

Compare the simulated output of a Nonlinear ARX Model block to the measured output of a system. You improve the agreement between the measured and simulated responses by estimating initial

Identify an idtf model using measured data. Convert it to state-space form to estimate the initial conditions consistent with that measured data. Then perform the same initial-condition

Reproduce command-line sim results for an estimated nonlinear system in Simulink. When you have an estimated system model, you can simulate it either with the command-line sim command or

Use two different techniques to estimate the initial conditions for a nonlinear ARX model. Simulate each alongside the measurement data, and compare with a simulation that initializes at

Design C-MEX model files that involve scalar, vector as well as matrix parameters. As a modeling basis, we will use a somewhat idealized industrial robot, where the left-hand sides of the

Use custom regressors in nonlinear ARX (IDNLARX) models, including single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems.

Identify the parameters of a complex yet artificial nonlinear discrete-time system with one input and one output. The system was originally proposed and discussed by Narendra and Li in the

Grey-box modeling of the dynamics of an industrial robot arm. The robot arm is described by a nonlinear three-mass flexible model according to Figure 1. This model is idealized in the sense

Identify single-input-single-output (SISO) nonlinear black box models using measured input-output data. The example uses measured data from a two-tank system to explore various model

Include and simulate an IDNLGREY model in Simulink®. We use a chemical reaction system as a modeling basis. The first modeling and identification part of the example can be run without

Nonlinear grey-box modeling of vehicle dynamics. Many new vehicle features (like Electronic Stability Programs (ESP), indirect Tire Pressure Monitoring Systems (TPMS), road-tire

Nonlinear black-box modeling of the dynamic behavior of a magneto-rheological fluid damper. It shows how to create Nonlinear ARX and Hammerstein-Wiener models of the damper using

Estimate parameters of a nonlinear grey box model using multiple experiment data. A system exhibiting dry friction between two solid bodies will be used as the basis for the discussion. In

Provide optional input arguments to IDNLGREY models. The discussion concentrates on how to do this for C-MEX types of model files, yet to some minor extent we will also address the most

How the estimation algorithm choices may impact the results for a nonlinear grey box model estimation. We use data produced by a nonlinear pendulum system, which is schematically shown in

Estimate multi-input multi-output (MIMO) nonlinear black box models from data. Two types of nonlinear black box models are offered in the toolbox - Nonlinear ARX and Hammerstein-Wiener

Perform IDNLGREY modeling based on C MEX model files. It uses a simple system where nonlinear state space modeling really pays off.

Construct, estimate and analyze nonlinear grey-box models.

Estimate Hammerstein-Wiener models using linear OE models.

Use nlarx to estimate a nonlinear ARX model for measured input/output data.

Write ODE files for nonlinear grey-box models as MATLAB and C MEX files.

Estimate and compare multiple Hammerstein-Wiener models using measured input-output data.

How the software evaluates the simulated output by first computing the output of the input and output nonlinearity estimators.

Use the Hammerstein-Wiener model structure to improve a previously estimated linear model.

The grey-box modeling of a large and complex nonlinear system. The purpose is to show the ability to use the IDNLGREY model to estimate a large number of parameters (16) in a system having many

Estimate nonlinear ARX models by using linear ARX models.

Grey-box modeling of a static single-input, single-output system using a MATLAB function as the ODE file.

Estimate states of linear systems using time-varying Kalman filters in Simulink. You use the Kalman Filter block from the System Identification Toolbox/Estimators library to estimate

Implement an online polynomial model estimator. You estimate two ARMAX models for a nonlinear chemical reaction process. These models capture the behavior of the process at two operating

Perform online parameter estimation for line-fitting using recursive estimation algorithms at the MATLAB command line. You capture the time-varying input-output behavior of the

Perform online parameter estimation for a time-varying ARX model at the MATLAB command line. The model parameters are updated at each time step with incoming new data. This model captures

Implement an online recursive least squares estimator. You estimate a nonlinear model of an internal combustion engine and use recursive least squares to detect changes in engine inertia.

Use the unscented Kalman filter and particle filter algorithms for nonlinear state estimation for the van der Pol oscillator.

Perform nonlinear state estimation in Simulink™ for a system with multiple sensors operating at different sample rates. The Extended Kalman Filter block in System Identification

Demonstrates the use of Particle Filter block in System Identification Toolbox™. A discrete-time transfer function parameter estimation problem is reformulated and recursively solved

Estimate the states of a discrete-time Van der Pol oscillator and compute state estimation errors and residuals for validating the estimation. The residuals are the output estimation

The modeling of a measured signal. We analyze the current signal from the R-phase when a 400 kV three-phase transformer is energized. The measurements were performed by Sydkraft AB in

Create a time series model and use the model for prediction, forecasting, and state estimation. The measured data is from an induction furnace whose slot size erodes over time. The slot size

Analyze time-series models.

Perform spectral estimation on time series data. We use Marple's test case (The complex data in L. Marple: S.L. Marple, Jr, Digital Spectral Analysis with Applications, Prentice-Hall,

Estimate Autoregressive Integrated Moving Average or ARIMA models.

Simulate a time-series model, compare the spectral estimates, estimate covariance, and predict output of the model.

Some methods for choosing and configuring the model structure. Estimation of a model using measurement data requires selection of a model structure (such as state-space or transfer

Estimate the parameters of a two-parameter system and compare the measured and estimated outputs.

Use frame-based signals with the Recursive Least Squares Estimator block in Simulink®. Machine interfaces often provide sensor data in frames containing multiple samples, rather than in

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

You can also select a web site from the following list:

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

Contact your local office