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.


Full text not available from this repository.


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 > Southampton Wireless Group
ePrint ID: 250115
Accepted Date and Publication Date:
Date Deposited: 04 May 1999
Last Modified: 31 Mar 2016 13:50
Further Information:Google Scholar

Actions (login required)

View Item View Item