The University of Southampton
University of Southampton Institutional Repository

Neurofuzzy state identification using prefiltering

Neurofuzzy state identification using prefiltering
Neurofuzzy state identification using prefiltering
A new state estimator algorithm is introduced based on a neurofuzzy network and the Kalman filter algorithm. The major contribution of the paper is recognition of a bias problem in the parameter estimation of the state space model and the introduction of a simple ffective pre-filtering method to achieve unbiased parameter estimates in the state space model which will then be applied for state estimation using the Kalman filtering algorithm. Fundamental to this method is a simple pre-filtering procedure using a non-linear principal component analysis PCAmethod based on the neuro-fuzzy basis set. This preltering procedure can be performed withoutprior system structure information. Some numerical examples are included to demonstrate theeffectiveness of the new approach.
1350-2379
234-240
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00

Hong, X., Harris, C.J. and Wilson, P.A. (1999) Neurofuzzy state identification using prefiltering. Control Theory and Applications, IEE Proceedings, 146 (2), 234-240. (doi:10.1049/ip-cta:19990121).

Record type: Article

Abstract

A new state estimator algorithm is introduced based on a neurofuzzy network and the Kalman filter algorithm. The major contribution of the paper is recognition of a bias problem in the parameter estimation of the state space model and the introduction of a simple ffective pre-filtering method to achieve unbiased parameter estimates in the state space model which will then be applied for state estimation using the Kalman filtering algorithm. Fundamental to this method is a simple pre-filtering procedure using a non-linear principal component analysis PCAmethod based on the neuro-fuzzy basis set. This preltering procedure can be performed withoutprior system structure information. Some numerical examples are included to demonstrate theeffectiveness of the new approach.

Other
xhcjhpaw98.ps - Author's Original
Download (340kB)

More information

Published date: March 1999
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 250664
URI: http://eprints.soton.ac.uk/id/eprint/250664
ISSN: 1350-2379
PURE UUID: 92c38405-5e3d-4e44-8b8d-910c164738fa
ORCID for P.A. Wilson: ORCID iD orcid.org/0000-0002-6939-682X

Catalogue record

Date deposited: 17 Sep 1999
Last modified: 07 Oct 2020 02:44

Export record

Altmetrics

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.

×