Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms. (11th January 2022)

• Record Type: Journal Article
• Authors: Ke, Zhihao; Liu, Xiaoning; Chen, Yining; Shi, Hongfu; Deng, Zigang
• Is Part Of: Superconductor science & technology. Volume 35:Number 2(2022)
• Publication Date: 2022-01-11
• Subjects: high-temperature superconducting maglev -- guiding force prediction -- deep learning algorithms -- neural network; Superconductivity -- Periodicals; Superconductors -- Periodicals; 537.623
• DOI: 10.1088/1361-6668/ac455d
• Languages: English
• ISSNs: 0953-2048

Abstract

Abstract: Due to the merits of self-stability and low energy consumption, high-temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes five different neural network models (radial basis function, deep neural network (DNN), convolution neural network, recurrent neural network, long short-term memory neural network (LSTM)) to predict HTS maglev guiding force and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptive iterative algorithms for the parameter matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. According to the results, it is revealed that the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90 000, respectively. Moreover, the K -fold cross-validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K -fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.