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 and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 250115 |
| Date Deposited: | 04 May 1999 |
| Last Modified: | 02 Mar 2012 13:39 |
| Contributors: | Wu, Z.Q. (Author) Harris, C.J. (Author) |
| Date: | 1996 |
| Additional Information: | Special Issue for Neurofuzzy'96, April, Prague |
| Status: | Published |
| Further Information: | Google Scholar |
| URI: | http://eprints.soton.ac.uk/id/eprint/250115 |
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