Distributed decision-making with learning threshold elements

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.<
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