Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter
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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
Free download from Shareware Connection - The Kalman filter can be interpreted as a feedback approach to minimize the least equare error
Version: 1.0 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 11-05-2013 | Rating: 0 | Title: Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter
Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/18356/1/ukfopt.zip
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