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Recurrent Neural Networks Training With Stable Bounding Ellipsoid Algorithm

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2 Author(s)
Wen Yu ; Dept. de Control Automatico, CINVESTAV-IPN, Mexico City ; JosÉ de Jesus Rubio

Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

Published in:

IEEE Transactions on Neural Networks  (Volume:20 ,  Issue: 6 )