BP Neural Network for Temperature Prediction of Alpha Magnetic Spectrometer on Orbit

Abstract

A BP neural network combined with the Adam optimization algorithm and the Mini-batches Learning algorithm was established for predicting the temperature of BOX-C on Alpha Magnetic Spectrometer (AMS) in this paper. After training, the Mean Squared Error (MSE) of the prediction results under the normal operating condition is 0.14 and this shows that the model can be used to predict the temperature of BOX-C with a satisfying accuracy. The model paves the ground for AMS thermal control on orbit.

Application of Neural Network Prediction in the Optimization of Forming Process of Magnetic Ceramic/Rare Earth Alloy Composite Magnets

“…At present, this technique can establish nonlinear correlations between output results and input parameters directly by means of experimental data samples without existing models. This platform is particularly suitable for predicting the nonlinear correlation between product performance and product preparation process parameters in production practice to guide production practice [1] . The composite magnet material is a kind of very important functional material which is formed and cured by mixing the magnetic powder with the binder.…”