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Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perceptron

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conference contribution
posted on 18.01.2010, 13:51 by Danilo P. Mandic, Jonathon Chambers
Conditions for global asymptotic stability (GAS) of a nonlinear relaxation equation realised by a nonlinear autoregressive moving average (NARMA) recurrent perceptron are provided. Convergence is derived through fixed point iteration (FPI) techniques, based upon a contraction mapping feature of a nonlinear activation function of a neuron. Furthermore, nesting is shown to be a spatial interpretation of an FPI, which underpins a pipelined recurrent neural network (PRNN) for nonlinear signal processing

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School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

MANDIC, D.P. and CHAMBERS, J., 1999. Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perceptron. IN: Proceedings of the 1999 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP '99, Phoenix, Arizona, 15th-19th March 1999, Vol. 2, pp. 1037-1040

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© IEEE

Version

VoR (Version of Record)

Publication date

1999

Notes

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

0780350413

Language

en

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