Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter
The Kalman filter can be interpreted as a feedback approach to minimize the least equare error. It can be applied to solve a nonlinear least square optimization problem. This function provides a way using the unscented Kalman filter to solve nonlinear least square optimization problems. Three examples are included: a general optimization problem, a problem to solve a set of nonlinear equations represented by a neural network model and a neural network training problem.
This function needs the unscented Kalman filter function, which can be download from the following link:
http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18217&objectType=FILE
Citation pour cette source
Yi Cao (2024). Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter (https://www.mathworks.com/matlabcentral/fileexchange/18356-nonlinear-least-square-optimization-through-parameter-estimation-using-the-unscented-kalman-filter), MATLAB Central File Exchange. Récupéré le .
Compatibilité avec les versions de MATLAB
Plateformes compatibles
Windows macOS LinuxCatégories
- Control Systems > System Identification Toolbox > Online Estimation >
- Mathematics and Optimization > Optimization Toolbox > Least Squares >
Tags
Remerciements
Inspiré par : Learning the Unscented Kalman Filter, Unconstrained Optimization using the Extended Kalman Filter
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Version | Publié le | Notes de version | |
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1.0.0.0 | update description |