Intelligent Neurofuzzy Estimators and Multisensor Data Fusion
Intelligent Neurofuzzy Estimators and Multisensor Data Fusion
Multi-Sensor Data Fusion (MSDF), or the process of fusing data from a variety of disparate data sources about a single entity, feature or system state, is of prime importance in the monitoring and control of complex systems. This paper addresses the basic subproblems of MSDF within a unified informational framework derived via Neurofuzzy modelling and estimation algorithms. This environment provides a common framework for integrating information which is database, sensor based, experiemental based and mechanistic. The paper introduces parsimonious neurofuzzy modelling algorithms and utilises them in generating local state estimators which are optimal in information processing. A MSDF system utilises these algorithms in a distributed decentralised architecture.
283-303
Kluwer Academic Publishers
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
1997
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J., Wu, Z.Q., Bossley, K.M. and Brown, M.
(1997)
Intelligent Neurofuzzy Estimators and Multisensor Data Fusion.
In,
Tzafestas, S.G.
(ed.)
Methods and Applications of Intelligent Control.
Methods and Applications of Intelligent Control (01/01/97)
Kluwer Academic Publishers, .
Record type:
Book Section
Abstract
Multi-Sensor Data Fusion (MSDF), or the process of fusing data from a variety of disparate data sources about a single entity, feature or system state, is of prime importance in the monitoring and control of complex systems. This paper addresses the basic subproblems of MSDF within a unified informational framework derived via Neurofuzzy modelling and estimation algorithms. This environment provides a common framework for integrating information which is database, sensor based, experiemental based and mechanistic. The paper introduces parsimonious neurofuzzy modelling algorithms and utilises them in generating local state estimators which are optimal in information processing. A MSDF system utilises these algorithms in a distributed decentralised architecture.
This record has no associated files available for download.
More information
Published date: 1997
Additional Information:
(no figures in on-line version) Chapter: 10 Address: Netherlands
Venue - Dates:
Methods and Applications of Intelligent Control, 1997-01-01
Organisations:
Electronics & Computer Science, IT Innovation, Southampton Wireless Group
Identifiers
Local EPrints ID: 250032
URI: http://eprints.soton.ac.uk/id/eprint/250032
PURE UUID: f29950f7-e881-4013-a5dc-d19c462a359b
Catalogue record
Date deposited: 21 Mar 2003
Last modified: 19 Mar 2024 17:43
Export record
Contributors
Author:
C.J. Harris
Author:
Z.Q. Wu
Author:
K.M. Bossley
Author:
M. Brown
Editor:
S.G. Tzafestas
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