RBFNN Node Importance Evaluation in Aviation Network Based on UKF Learning Algorithm
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Haiqing Huang, Xusheng Gan, Zimeng Sun, Zhibin Chen
To evaluate the nodes in aviation network for key nodes identification, a RBFNN (RBFNN) node evaluation method using UKF learning algorithm is proposed for aviation network. RBFNN based on UKF learning algorithm (UKF-RBFNN) is firstly studied for evaluation modeling, and then UKF-RBFNN model, with the simple indices of network node as the inputs, and the comprehensive importance of node by complex indices as the outputs, is established. Simulation shows that the proposed method is effective and feasible for Node importance evaluation in aviation network.
Aviation Network, RBFNN, Unscented Kalman Filter, Node Importance Evaluation, Comprehensive Importance