Training multilayer perceptrons with the extended Kalman algorithm
Advances in neural information processing systems 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Dual extended Kalman filtering in recurrent neural networks
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Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
A real-time learning algorithm for a multilayered neural networkbased on the extended Kalman filter
IEEE Transactions on Signal Processing
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IEEE Transactions on Signal Processing
On the Kalman filtering method in neural network training and pruning
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
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IEEE Transactions on Neural Networks
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ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Applied Soft Computing
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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 state-space recurrent neural networks for nonlinear system identification. In order to improve robustness of Kalman filter algorithm dead-zone robust modification is applied to Kalman filter. Lyapunov method is used to prove that the Kalman filter training is stable.