The University of Southampton
University of Southampton Institutional Repository

Neurofuzzy Modelling and State Estimation

Record type: Conference or Workshop Item (Other)

It is of great practical significance to merge the neural network identification technique and the Kalman filter to achieve optimal adaptive filtering and prediction for unknown observable nonlinear processes. In this paper, an operating point dependent ARMA model is used to represent the nonlinear system, and a neurofuzzy network is used to identify this model. It is then converted to its equivalent state-space representation with which a Kalman filter is applied to perform state estimation. Two approaches to combine the neurofuzzy modelling and the Kalman filter algorithm, indirect method and direct method, are presented. A simulated example is also given.

Full text not available from this repository.

Citation

Wu, Z.Q. and Harris, C.J. (1996) Neurofuzzy Modelling and State Estimation At IEEE Medit. Symp. on Control and Automation: Circuits, Systems and Computers '96. , pp. 603-610.

More information

Published date: July 1996
Additional Information: Address: Hellenic Naval Academy, Piraeus, Greece
Venue - Dates: IEEE Medit. Symp. on Control and Automation: Circuits, Systems and Computers '96, 1996-07-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250010
URI: http://eprints.soton.ac.uk/id/eprint/250010
ISBN: 960-8485-00-2
PURE UUID: bc2de7c4-09e1-41db-ac2e-ecc5584c45a0

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:44

Export record

Contributors

Author: Z.Q. Wu
Author: C.J. Harris

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×