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

Abstract

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.