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Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train recurrent wavelets neural networks for nonlinear system identification. In order to improve robustness of Kalman...
This paper presents a sequential growing-and-pruning learning algorithm employing an unscented or extended Kalman filter (SGAPL-UKF or SGAPL-EKF) for a recurrent neural network (RNN). The RNN is constructed using a sequential-learning algorithm that employs growing-and-pruning (GAP) criteria based on the concept of the significance of hidden neurons to yield a compact network; and an unscented or...
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