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.


Full text not available from this repository.


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.

Item Type: Book Section
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
ePrint ID: 250032
Date :
Date Event
Date Deposited: 21 Mar 2003
Last Modified: 18 Apr 2017 00:24
Further Information:Google Scholar

Actions (login required)

View Item View Item