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:
Yi Cao (2023). Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter (https://www.mathworks.com/matlabcentral/fileexchange/18356-nonlinear-least-square-optimization-through-parameter-estimation-using-the-unscented-kalman-filter), MATLAB Central File Exchange. Retrieved .
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