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Intelligent Neurofuzzy Estimators and Multisensor Data Fusion

Harris, C.J., Wu, Z.Q., Bossley, K.M. and Brown, M. (1997) Intelligent Neurofuzzy Estimators and Multisensor Data Fusion In, Tzafestas, S.G. (eds.) Methods and Applications of Intelligent Control. Kluwer Academic pp. 283-303.

Record type: Book Section


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.

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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


Local EPrints ID: 250032
PURE UUID: f29950f7-e881-4013-a5dc-d19c462a359b

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Date deposited: 21 Mar 2003
Last modified: 18 Jul 2017 10:44

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Author: C.J. Harris
Author: Z.Q. Wu
Author: K.M. Bossley
Author: M. Brown
Editor: S.G. Tzafestas

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