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:
Yi Cao (2021). Unconstrained Optimization using the Extended Kalman Filter (https://www.mathworks.com/matlabcentral/fileexchange/18286-unconstrained-optimization-using-the-extended-kalman-filter), MATLAB Central File Exchange. Retrieved .
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Inspired by: Learning the Extended Kalman Filter
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