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

设计基于卷积神经网络优化算法的列车智能测试系统,解决城市轨道交通领域列车系统测试自动化模拟问题。提出的列车智能测试系统,采用卷积神经网络的结构模型和基于分层压缩的卷积神经网络算法,详尽介绍构建分层压缩卷积神经网络的具体过程和卷积核优化结构设计。对站场测试用例的自动化模拟实验和测试数据分析的结果表明,基于卷积神经网络优化算法的列车智能测试系统可以优化测试过程、降低人工错误操作、合理分配测试资源、提高测试质量,加快整体系统测试进度的要求,为城市轨道交通领域未来实现全面自动化测试提供技术保障。

Train intelligent testing system based on convolution neural network optimization algorithm

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.