Open AccessContribution Assessment Approach for Command and Control System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks
Author(s) -
Peng Liu,
Zeyu Zhou,
Ning Li,
Bowei Zhang,
Mei Han,
Huiying Jiao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2003/1/012010
Subject(s) - encoding (memory) , artificial neural network , computer science , process (computing) , artificial intelligence , control (management) , control system , data mining , control engineering , engineering , electrical engineering , operating system
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.