Thermal equilibrium temperature prediction based on process neural network

In order to shorten the duration of the thermal balance test of the spacecraft to reduce the development cost of the spacecraft, a thermal equilibrium temperature prediction model based on the process neural network is proposed. To simplify the learning procedure of the proposed prediction model, a basic learning algorithm based on the expansion of the orthogonal basis functions is given. With the orthogonality of the orthogonal basis functions, the time aggregation operation in the proposed model can be simplified and the adaptability of the proposed model to the practical problem resolving can be raised. Furthermore, in order to reinforce the extrapolation capability of the proposed prediction model, a learning algorithm for new pattern based on the basic learning algorithm is developed, which can learn the new patterns quickly without degrading the recall of the old patterns. The application test results indicate that the proposed prediction model can utilize the first 40 hours stable test data in the thermal test on some monitoring point of some type satellite to obtain the ultimate thermal equilibrium temperature of the monitoring point about 42.5 to 68 hours in advance.