Abstract
Aiming at the difficulty of feature extraction and related generation mechanism analysis for Command and Control (C2) System using traditional data mining method, a novel contribution assessment approach based on Force-Sparsed Stacked-Auto Encoding Neural Networks (FS-SAE) is proposed. Combined with big data and complex networks technology, the contribution assessment model to operational system of system (SoS) is built. The emergence relations between the capacity indices of C2 system are formalized. The derivation results show that formalized presentation for the emergence process of performance indices of C2 system based on the proposed model not only reflects the complexity characteristics of non-linear and uncertainty in emergence process, but also gives general-defined meaning for indices structure of C2 system. It provides a feasible method for the commanders to deeply understand, manage and control the complex operation system.
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