Abstract
In this paper, we present a Functional Link Network (FLN) based neural technique for sensors issued data fusion. Thanks to a pruning algorithm, we build dynamically the internal layer of the FLN, constructing an optimal architecture of the FLN neural network defining an optimal fusion policy. As the neural FLN minimize the mean square error (MSE) during the learning step, an optimal fusion policy is reached in the sense of the MSE. Experimental results related to performances enhancement of metric sensors have been reported validating our approach.KeywordsMean Square ErrorData FusionRadial Basis FunctionPruning AlgorithmOptimal ArchitectureThese 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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