Due to the existence of a complex spatial and temporal correlation

visualization and information related dynamic constraints to each equipment when they executed TT&C(telemetry tracking & commanding) mission

it was extremely difficult to model and analyze

and the solving efficiency was low.Therefore a radial basis function neural network (RBFNN) modeling method with adaptive hybrid learning(AHL) algorithm was proposed.Principal component analysis were used to determine the initial number of hidden units.Intelligence optimization algorithm combined with decaying gradient descent information was used to train parameters of RBFNN which were improved to accelerate convergence and guarantee the result to converge at the global optimum with high probability.The algorithm was employed in the offline training of model parameters by sampling the input/output data of the system

and the realization details were also provided.By simulation

it shows that the AHL has higher generalization power and can reach more optimal results in solving the mission reliability allocation of spaceflight TT&C system.