基于卷积神经网络优化算法的列车智能测试系统技术研究 ( Train intelligent testing system based on convolution neural network optimization algorithm )

Chao Wang

Abstract: An intelligent train testing system based on image recognition technology was designed to solve the problem of automatic simulation of train system test in urban rail transit. The intelligent train test system proposed in this paper adopted the structure model of convolution neural network and convolutional neural network algorithms based on hierarchical compression. The paper introduced in detail the concrete process of constructing layered compression convolution neural network and the optimal structure design of convolution core. Through the analysis of automated simulation experiment and test data of station and yard test cases, the results show that the train intelligent testing system based on convolution neural network optimization algorithm can optimize the test process, reduce manual error operation, rationally allocate test resources, improve test quality, speed up the overall system test schedule requirements. The system can also provide technical support for the implementation of comprehensive automated testing in the field of urban rail transit in the future.