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Modeling and algorithm to mission reliability allocation of spaceflight TT&C system based on radial basis function neural network

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2 Author(s)
Xingui Zhang ; College of Information Systems and Management, National University of Defense Technology, Changsha, China ; Xiaoyue Wu

To study mission reliability allocation of the tracking, telemetry and command (TT&C) system, which is difficult to describe with a precise mathematical model and time-consumed to compute, a radial basis function neural network (RBFNN) modeling method with adaptive hybrid learning algorithm (AHL) is proposed. Principal component analysis (PCA) is used to determine the initial number of hidden units. Advanced gradient learning algorithm (AGL) to compute gradient information of network parameters is improved to accelerate convergence. Finally, realization details are provided, and simulation results show the effectiveness of the proposed method.

Published in:

Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on

Date of Conference:

15-18 June 2012