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