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Associative Memory Network Based Modelling of Unknown Nonlinear Systems subject to Immeasurable Disturbances

Record type: Article

This paper presents a neural network based scheme for modelling unknown nonlinear systems subject to immeasurable disturbances which satisfy stable, finite-order, recurrence relationships whose parameters are known. The systems considered can be expressed as nonlinear ARMAX models and the disturbance is non-stochastic. Similar to robust servomechanism design, the nonlinear modes of the disturbance are assumed to be known and based upon the knowledge of these modes, a new performance function for modelling the unknown nonlinear function is selected and a gradient descent algorithm which adjusts the weights in the neural network is derived. Convergence of this learning algorithm is proved when the disturbance satisfies a linear recurrence relationship, and the proposed approach is used to mdoel nonlinear time series data which has been corrupted by immeasurable additive sinusoidal noise.

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Citation

Wang, H., Brown, M. and Harris, C.J. (1994) Associative Memory Network Based Modelling of Unknown Nonlinear Systems subject to Immeasurable Disturbances IEE Part D, 141, (4), pp. 216-222.

More information

Published date: 1994
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250219
URI: http://eprints.soton.ac.uk/id/eprint/250219
PURE UUID: d38a51fa-8b7d-4787-b144-da20193970c2

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Contributors

Author: H. Wang
Author: M. Brown
Author: C.J. Harris

University divisions


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