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Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series

Record type: Article

Some classes of nonlinear systems or time series can be represented by an operating point dependent ARMA model. In this paper a neurofuzzy network structure is configured to identify such a model and the neural network is trained by the normalized back-propagation algorithm. The identified model is then converted to its equivalent state-space representation. Using this state-space form, a Kalman filter can be applied to estimate the state indirectly. A simulated example is given.

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Citation

Wu, Z.Q. and Harris, C.J. (1996) Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series Neural Network World, 6, (3), 407--416.

More information

Published date: 1996
Additional Information: Special Issue for Neurofuzzy'96, April, Prague
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250115
URI: http://eprints.soton.ac.uk/id/eprint/250115
PURE UUID: 92220e1a-f46a-453d-95b2-0db440b1dd74

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:44

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Contributors

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

University divisions


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