The feasibility of designing a back propagation neural network to predict the levitation force of high-temperature superconducting magnetic levitation

Abstract

Abstract The levitation force between the superconductor and the magnet is highly nonlinear and affected by the coupling of multiple factors, which brings many obstacles to research and application. In addition to experimental methods and finite element simulations, the booming artificial neural network (ANN) which is adept at continuous nonlinear fitting may provide another solution to predict the levitation force. And this topic has not been deeply investigated so far. Therefore, this study aims to apply the ANN to predict the levitation force, and a typical neural network applied with the back propagation (BP) is adopted. The data set with 2399 pieces of data considers nine input factors and one force output, which was experimentally obtained by several test devices. The pre-process of the data set contains cleaning, balancing, one-hot encoding (for the discrete classified variable), normalization (for the continuous variable) and randomization. A classical perception with three layers (input, hidden and output layer) is applied in this paper. And the gradient descent back propagation algorithm reduces the error by iteration. Through the assessment and evaluation of the network, a great prediction accuracy could achieve. The prediction results could well illustrate the features of force (nonlinear, hysteresis, external field dependence and type difference between the bulk and stack), which confirm the feasibility of using a BP neural network to predict the levitation force. Furthermore, the performance of the neural network is determined by the data set, especially the uniformity and balance among factors in the set. Moreover, the huge gap in the quantity of data between factors disturbs the network to make a comprehensive judgment, and in this situation, the binary one-hot encoding of the small quantity and discrete data factor is efficient, instead of the actual value of the factor, the one-hot encoded data only represent the category. Moreover, a label encoder method is adopted to distinguish the decent and ascend (decent = 1, ascent = 0) for the force hysteresis.