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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 effective 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 PCA method based on the neuro-fuzzy basis set. This prefiltering procedure can be performed without prior system structure information. Some numerical examples are included to demonstrate the effectiveness 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 effective 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 PCA method based on the neuro-fuzzy basis set. This prefiltering procedure can be performed without prior system structure information. Some numerical examples are included to demonstrate the effectiveness of the new approach.

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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: 09 Jan 2022 02:34

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Contributors

Author: X. Hong
Author: C.J. Harris
Author: P.A. Wilson ORCID iD

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