Distributed decision-making with learning threshold elements

K. Atteson, M. Schrier, G. Lipson, M. Kam

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

The authors discuss the application of networks of learning threshold elements in decision making for systems with distributed sensors. A data fusion center receives the decision of n independent sensors regarding a set of hypotheses and makes a 'global' decision. The authors use results of studies by R.R. Tenney and N.R. Sandell (1981) and Z. Chair and P.K. Varshney (1986) of the optimal 'local' and 'global' decision rules. However, the authors do not assume a priori knowledge of the hypothesis and the communication-channel statistics. A simple updating rule is used to estimate the unknown probabilities and to tune the weights of the threshold elements. Using a simple two-hypothesis example, the authors demonstrate how the learning system approximates the optimal performance and how it can partially recover from sensor failure.

Original languageEnglish (US)
Pages (from-to)804-805
Number of pages2
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - Dec 1 1988
Externally publishedYes
EventProceedings of the 27th IEEE Conference on Decision and Control - Austin, TX, USA
Duration: Dec 7 1988Dec 9 1988

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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