Indirect Adaptive Neurofuzzy Estimation of Nonlinear Time Series


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

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Description/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.

Item Type: Article
Additional Information: Special Issue for Neurofuzzy'96, April, Prague
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250115
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250115

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