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ISNN
2007
Springer

Neural Networks Training with Optimal Bounded Ellipsoid Algorithm

10 years 8 months ago
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
Abstract. Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural networks for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic systems point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. A simple simulation gives the effectiveness of the suggested algorithm.
José de Jesús Rubio, Wen Yu
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ISNN
Authors José de Jesús Rubio, Wen Yu
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