Abstract
An urban subway network system is a complex public transportation system. To compare rail transit braking digital command control strategies based on neural network, this article analyzes and studies the characteristics of subway vehicle driver controllers and their design methods from three aspects: mechanical, electrical and software-assisted design. The learning rule of the BP neural network is called the mentor system learning rule, which is a kind of error-correcting algorithm. In the learning and training process, the expected output value needs to be given. The weights and thresholds of the BP neural network are optimized by selecting the parameters of the SA algorithm. The search method of SA is heuristic, and it has the following advantages: The selection of the initial solution does not affect the optimal solution. The simplified model extracts the core data processing individual analysis. In this paper, the physical data are extracted from the physical entity operation process for analysis, and the twin model is established to extract the twin data for analysis. This paper uses the characteristics of physical data to test the modeling effect and utilizes the twin data to carry out algorithm experiments on physical data. The ultimate goal is to use twin data to predict the state information of physical entities. The network error in the scheme designed by the article is 6%. The smooth implementation of this research constitutes an important reference for the design of subway train network control systems in other cities in China. Therefore, this research has great application value.
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Fan, Z., Huang, D., Xu, K. et al. Comparative analysis of rail transit braking digital command control strategies based on neural network. Neural Comput & Applic 35, 8833–8845 (2023). https://doi.org/10.1007/s00521-022-07552-3
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DOI: https://doi.org/10.1007/s00521-022-07552-3