A normalized gradient algorithm for an adaptive recurrent perceptron
conference contribution
posted on 2010-01-15, 10:02 authored by Jonathon Chambers, Warren Sherliker, Danilo P. MandicA 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. 396-399Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2000Notes
This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.ISBN
0780362934Language
- en
