Modelling and Adaptive Filtering of Nonlinear Systems Using Neural Network
Wu, Z.Q. and Harris, C.J. (1995) Modelling and Adaptive Filtering of Nonlinear Systems Using Neural Network.
Full text not available from this repository.
For some classes of nonlinear systems or time series, an operating point dependent ARMA model in which the parameters are some kind of nonlinear functions of the operating point can be used to represent the system. In this paper we use the neural networks to identify such a model which is then converted to its equivalent state-space representation. Using the state-space model form, we are able to design a Kalman filter to conduct the state estimate. The neural network and estimator are tested on a set of input output data recorded from an actual electric heater which is a non-linear system.
|Item Type:||Monograph (Technical Report)|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
|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.|
Actions (login required)