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Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter 1.0

  Date Added: July 28, 2013  |  Visits: 238

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

Requirements: No special requirements
Platforms: Matlab
Keyword: Download Equations Examples General Included Minimize Model Network Neural Problems Problemthis Represented Training Unscented
Users rating: 0/10

License: Freeware Size: 10 KB
NONLINEAR LEAST SQUARE OPTIMIZATION THROUGH PARAMETER ESTIMATION USING THE UNSCENTED KALMAN FILTER RELATED
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