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Modelling and Adaptive Filtering of Nonlinear Systems Using Neural Network

Record type: Monograph (Project Report)

For some classes of nonlinear systems or time series, an operating point dependent ARMA model in which the parameters are some kind of nonlinear functions of the operating point can be used to represent the system. In this paper we use the neural networks to identify such a model which is then converted to its equivalent state-space representation. Using the state-space model form, we are able to design a Kalman filter to conduct the state estimate. The neural network and estimator are tested on a set of input output data recorded from an actual electric heater which is a non-linear system.

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Citation

Wu, Z.Q. and Harris, C.J. (1995) Modelling and Adaptive Filtering of Nonlinear Systems Using Neural Network s.n.

More information

Published date: 1995
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250139
URI: http://eprints.soton.ac.uk/id/eprint/250139
PURE UUID: 7b18eec6-d9f8-4c9e-9e89-af8c401d24e0

Catalogue record

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

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Contributors

Author: Z.Q. Wu
Author: C.J. Harris

University divisions


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