Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter (Scripts) Publisher's description
from Yi Cao
The Kalman filter can be interpreted as a feedback approach to minimize the least equare error
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:
System Requirements:MATLAB 7.4 (R2007a)
Program Release Status: New Release
Program Install Support: Install and Uninstall