Data Fusion & Neural Networks (DF&NN) provides custom design and development for Data Fusion & Resource Management (DF&RM) software applications using model-driven, data-driven, and goal-driven techniques include Neural Networks (NNs), Bayesian, possibilistic, and rule-based paradigms. DF&NN has delivered affordable operational prototype Condition-Based Health Management (CBHM) software for on-line applications. This data-driven abnormality detection, characterization, and event tracking software learns normal behavior so as to detect unexpected abnormally correlated behavior for many thousands of inputs in real-time. DF&NN has applied its Dual Node Network (DNN) DF&RM technical architecture to provide problem-to-solution space guidelines to interlace DF nodes with ‘dual’ RM nodes. This DNN technical architecture provides the hierarchical component decomposition of Data Fusion & Resource Management (DF&RM) and extends the duality of estimation and control used to ‘bootstrap’ progress in control back in the 1960′s.

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