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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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.
|Additional Information:||Special Issue for Neurofuzzy'96, April, Prague|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||04 May 1999|
|Last Modified:||27 Mar 2014 19:51|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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