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Online Parameter Estimation

Estimate model parameters using recursive algorithms at the command line and in Simulink®

You can estimate parameters of AR, ARMA, ARX, ARMAX, OE, or BJ model coefficients using real-time data and recursive algorithms. You can also estimate models using a recursive least squares (RLS) algorithm. For details about the algorithms, see Recursive Algorithms for Online Parameter Estimation.

You can perform online parameter estimation using Simulink blocks in the Estimators sublibrary of the System Identification Toolbox™ library. You can then generate C/C++ code and Structured Text for these blocks using Simulink Coder™ and Simulink PLC Coder™, and deploy this code to an embedded target. You can also perform online estimation at the command line and deploy your code using MATLAB® Compiler™ or MATLAB Coder.

Functions

recursiveARCreate System object for online parameter estimation of AR model
recursiveARMACreate System object for online parameter estimation of ARMA model
recursiveARXCreate System object for online parameter estimation of ARX model
recursiveARMAXCreate System object for online parameter estimation of ARMAX model
recursiveBJCreate System object for online parameter estimation of Box-Jenkins polynomial model
recursiveOECreate System object for online parameter estimation of Output-Error polynomial model
recursiveLSCreate System object for online parameter estimation using recursive least squares algorithm
stepUpdate model parameters and output online using recursive estimation algorithm
cloneCopy online parameter estimation System object
resetReset online parameter estimation System object
releaseUnlock online parameter estimation System object
isLockedLocked status of online parameter estimation System object
rpemEstimate general input-output models using recursive prediction-error minimization method
rplrEstimate general input-output models using recursive pseudolinear regression method
segmentSegment data and estimate models for each segment

Blocks

Recursive Least Squares EstimatorEstimate model coefficients using recursive least squares (RLS) algorithm
Recursive Polynomial Model EstimatorEstimate input-output and time-series polynomial model coefficients
Model Type ConverterConvert polynomial model coefficients to state-space model matrices

Topics

Online Estimation Basics

What Is Online Estimation?

Estimate states and parameters of a system in real-time.

How Online Parameter Estimation Differs from Offline Estimation

Difference in data, algorithms, and estimation implementations.

Recursive Algorithms for Online Parameter Estimation

Forgetting factor, Kalman filter, and gradient and unnormalized gradient algorithms for online parameter estimation.

Online Parameter Estimation in Simulink

Preprocess Online Parameter Estimation Data in Simulink

Remove drift, offset, missing samples, seasonalities, equilibrium behavior, and outliers in your data.

Online Recursive Least Squares Estimation

This example shows how to implement an online recursive least squares estimator.

Online ARMAX Polynomial Model Estimation

This example shows how to implement an online polynomial model estimator.

Validate Online Parameter Estimation Results in Simulink

Examine estimation errors, parameter covariance, and difference between simulated and measured outputs.

Online Parameter Estimation at the Command Line

Perform Online Parameter Estimation at the Command Line

Online parameter estimation using System Objects.

Online ARX Parameter Estimation for Tracking Time-Varying System Dynamics

This example shows how to perform online parameter estimation for a time-varying ARX model at the MATLAB command line.

Line Fitting with Online Recursive Least Squares Estimation

This example shows how to perform online parameter estimation for line-fitting using recursive estimation algorithms at the MATLAB command line.

Validate Online Parameter Estimation at the Command Line

Examine estimation errors, parameter covariance, and difference between simulated and measured outputs.

Data Segmentation

Use of data segmentation to model systems exhibiting abrupt changes.

Detect Abrupt System Changes Using Identification Techniques

This example shows how to detect abrupt changes in the behavior of a system using online estimation and automatic data segmentation techniques.

Code Generation

Generate Online Parameter Estimation Code in Simulink

Generate C/C++ code and Structured Text using Simulink Coder and Simulink PLC Coder products.

Generate Code for Online Parameter Estimation in MATLAB

Generate C/C++ code using MATLAB Coder software; limitations for System objects.

Troubleshooting

Troubleshoot Online Parameter Estimation

Check your model, estimation data, estimation settings, and initial parameter values.

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