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Unconstrained Optimization using the Extended Kalman Filter

3.5 | 2 ratings Rate this file 21 Downloads (last 30 days) File Size: 2.08 KB File ID: #18286 Version: 1.0

Unconstrained Optimization using the Extended Kalman Filter


Yi Cao (view profile)


10 Jan 2008 (Updated )

A function using the extended Kalman filter to perform unconstrained nonlinear optimization

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The Kalman filter is actually a feedback approach to minimize the estimation error in terms of sum of square. This approach can be applied to general nonlinear optimization. This function shows a way using the extended Kalman filter to solve some unconstrained nonlinear optimization problems. Two examples are included: a general optimization problem and a problem to solve a set of nonlinear equations represented by a neural network model.

This function needs the extended Kalman filter function, which can be download from the following link:


Learning The Extended Kalman Filter inspired this file.

This file inspired Nonlinear Least Square Optimization Through Parameter Estimation Using The Unscented Kalman Filter.

MATLAB release MATLAB 7.5 (R2007b)
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Comments and Ratings (3)
28 Apr 2009 Rohit Hippalgaonkar

Hi I am looking for an example where the EKF is applied to a continuous-time non-linear system with non-zero inputs (say measurements are taken at regular time samples through a non-linear (even linear would do) measurement process. I have looked around for this kind of example in the standard texts but haven't found any.

Also a good source showing the implementation of the EKF wherein we linearize about a single operating point (as against linearizing about the predicted state every time) would be really helpful!

Thanks in advance!

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05 Apr 2009 V. Poor

20 Jan 2008 sudheer ch

Most of the times gets caught with local minima. it needs a lot of improvement.

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