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Improvements to the GRP1 Combination Rule

  • Gavin Powell
  • Matthew Roberts
  • Dafni Stampouli
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

The recursive use of belief function combination rules, as required with temporal data, is issue prone. Systems will either become unreactive, through a greedy empty set, or provide a false sense of security through applying a closed world model to an open world scenario. We improve on the previous combination rule GRP1 to enhance its ability to work with temporal data in an open world. Specifically we have progressed with the dynamic self adjustment properties of the rule, which allow it to gauge how fusion should take place dependant on the temporal information that it receives. Comparisons are made between the improved GRP1 rule and other rules which have been applied to temporal datasets.

Keywords

Wireless Sensor Network Temporal Information Information Fusion Combination Rule Belief Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gavin Powell
    • 1
  • Matthew Roberts
    • 1
  • Dafni Stampouli
    • 1
  1. 1.EADS Innovation WorksNewportUnited Kingdom

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