Stima online dei parametri
È possibile stimare i parametri dei coefficienti dei modelli AR, ARMA, ARX, ARMAX, OE o BJ utilizzando dati in tempo reale e algoritmi ricorsivi. È inoltre possibile stimare i modelli utilizzando un algoritmo dei minimi quadrati ricorsivo (RLS). Per i dettagli sugli algoritmi, vedere Recursive Algorithms for Online Parameter Estimation.
È possibile eseguire la stima online dei parametri utilizzando i blocchi Simulink della libreria secondaria Estimators della libreria System Identification Toolbox™. È quindi possibile generare codice C/C++ e testo strutturato per questi blocchi utilizzando Simulink Coder™ e Simulink PLC Coder™ e distribuire questo codice su un target embedded. È inoltre possibile eseguire la stima online dalla riga di comando e distribuire il codice utilizzando MATLAB® Compiler™ o MATLAB Coder.
Funzioni
recursiveAR | Online parameter estimation of AR model |
recursiveARMA | Online parameter estimation of ARMA model |
recursiveARX | Online parameter estimation of ARX model |
recursiveARMAX | Online parameter estimation of ARMAX model |
recursiveBJ | Online parameter estimation of Box-Jenkins model |
recursiveOE | Online parameter estimation of output-error polynomial model |
recursiveLS | Online parameter estimation of least-squares model |
rpem | Estimate general input-output models using recursive prediction-error minimization method |
rplr | Estimate general input-output models using recursive pseudolinear regression method |
segment | Segment data and estimate models for each segment |
Blocchi
| Recursive Least Squares Estimator | Estimate model coefficients using recursive least squares (RLS) algorithm |
| Recursive Polynomial Model Estimator | Estimate input-output and time-series polynomial model coefficients |
| Model Type Converter | Convert polynomial model coefficients to state-space model matrices |
Argomenti
Nozioni di base sulla stima online
- 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, gradient and unnormalized gradient, and finite-history algorithms for online parameter estimation.
Stima online dei parametri 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.
Stima online dei parametri dalla riga di comando
- Perform Online Parameter Estimation at the Command Line
Online parameter estimation using System Objects. - Online ARX Parameter Estimation for Tracking Time-Varying System Dynamics
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.
Generazione di codice
- 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.
Risoluzione dei problemi
Troubleshoot Online Parameter Estimation
Check your model, estimation data, estimation settings, and initial parameter values.
