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The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). The error of the MEKF algorithm is proved to be exponentially(More)
Learning for feedforward neural networks can be regarded as a nonlinear parameter estimation problem with the objective of finding the optimal weights that provide the best fitting of a given training set. The extended Kalman filter is well-suited to accomplishing this task, as it is a recursive state estimation method for nonlinear systems. Such a training(More)
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