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Unconstrained Optimization using the Extended Kalman Filter

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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:

Comments and Ratings (3)

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!

V. Poor

sudheer ch

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

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