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|Title: ||A normalized gradient algorithm for an adaptive recurrent perceptron|
|Authors: ||Chambers, Jonathon|
Mandic, Danilo P.
|Issue Date: ||2000|
|Publisher: ||© IEEE|
|Citation: ||CHAMBERS, J.A., SHERLIKER, W. and MANDIC, D.P., 2000. A normalized gradient algorithm for an adaptive recurrent perceptron. IN: IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP '00), Istanbul, 5-9 June, Vol. 1, pp. 396-399|
|Abstract: ||A normalized algorithm for on-line adaptation of a recurrent perceptron is derived. The algorithm builds upon the normalized backpropagation (NBP) algorithm for feedforward neural networks, and provides an adaptive learning rate and normalization for a recurrent perceptron learning algorithm. The algorithm is based upon local linearization about the current point in the state-space of the network. Such a learning rate is normalized by the squared norm of the gradient at the neuron, which extends the notion of normalized linear algorithms to the nonlinear case|
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|Appears in Collections:||Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)|
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