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A normalized gradient algorithm for an adaptive recurrent perceptron

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conference contribution
posted on 15.01.2010, 10:02 by Jonathon Chambers, Warren Sherliker, Danilo P. Mandic
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

History

School

  • Mechanical, Electrical and Manufacturing Engineering

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

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2000

Notes

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ISBN

0780362934

Language

en

Exports

Loughborough Publications

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