Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series
Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series
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.
407--416
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1996
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wu, Z.Q. and Harris, C.J.
(1996)
Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series.
Neural Network World, 6 (3), .
Abstract
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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Published date: 1996
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Special Issue for Neurofuzzy'96, April, Prague
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250115
URI: http://eprints.soton.ac.uk/id/eprint/250115
PURE UUID: 92220e1a-f46a-453d-95b2-0db440b1dd74
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Date deposited: 04 May 1999
Last modified: 07 Jan 2022 21:08
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Author:
Z.Q. Wu
Author:
C.J. Harris
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