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:
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!
Most of the times gets caught with local minima. it needs a lot of improvement.
Inspired by: Learning the Extended Kalman Filter
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