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| Paper IPM / Cognitive / 11398 |
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Modeling and assessing sensor reliabilities is one of the most important problems in the information fusion. Learning reliability coefficients in the uncertainty model of a distributed sensor network is a difficult nonlinear optimization problem. However, utilizing more flexible models such as belief functions and considering precise error functions leads to achieve more accurate solutions, makes the problem more complicated. Evolutionary algorithms are known to be powerful tools to solve nonlinear optimization problems. Above mentioned facts about the learning of sensor reliabilities make the evolutionary algorithms a suitable option. Population Based Incremental Learning (PBIL) is a rather new evolutionary algorithm which works better than genetic algorithms in most of the optimization problems. In this paper, we develop an extension of the standard PBIL algorithm to continuous search spaces for the constrained nonlinear optimization problem of the learning sensor reliabilities. We apply this algorithm to decision fusion based on utility function and examine its performance over a synthetic data.
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