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首页> 外文会议>ASME Rail Transportation Division Conference >A METHODOLOGY FOR THE MODELING OF RAIL VEHICLES FROM TIME SERIES MEASUREMENTS USING TIME-DELAY NEURAL NETWORKS
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A METHODOLOGY FOR THE MODELING OF RAIL VEHICLES FROM TIME SERIES MEASUREMENTS USING TIME-DELAY NEURAL NETWORKS

机译：使用时间延迟神经网络从时间序列测量建模的方法

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摘要

The main goal of this research is to develop and demonstrate a general, efficient, mathematically and theoretically based methodology to model nonlinear forced vibrating mechanical systems from time series measurements. A system identification modeling methodology for forced dynamical systems is presented based on dynamic system theory and nonlinear time series analysis that employs phase space reconstruction (delay vector embedding) for modeling of dynamical systems from time series data using time-delay neural networks (TDNN). The first part of this work details the modeling methodology including background on dynamic systems, phase space reconstruction, and neural networks. In the second part of this work the methodology is evaluated based on its ability to model selected analytical lumped parameter forced vibrating dynamic systems including an example of a linear system predicting lumped mass displacement using a displacement forcing. function The work discusses the application to nonlinear systems, multi degree-of-freedom systems, and multi-input systems. The methodology is further evaluated on its ability to model an analytical passenger rail vehicle predicting vertical wheel/rail force using vertical rail profile as input. Studying the neural modeling methodology using an analytical systems shows the clearest observations from results which provide prospective users of this tool an understanding of the expectations and limitations of the modeling methodology.
机译：本研究的主要目标是开发和展示一种从时序测量的模型非线性强制振动机械系统的一般，高效，数学和理论上的方法。基于动态系统理论和非线性时间序列分析来提出了一种用于强制动态系统的系统识别模型方法，其采用相位空间重建（延迟向量嵌入）来使用时延神经网络（TDNN）从时间序列数据建模动态系统。这项工作的第一部分详细介绍了模型方法，包括动态系统，相空间重建和神经网络的背景。在这项工作的第二部分中，根据其模拟所选择的分析集成参数强制振动动力系统的能力来评估方法，包括使用位移强制预测集成的质量位移的线性系统的示例。功能该工作讨论了非线性系统，多自由度系统和多输入系统的应用。进一步评估了使用垂直轨道轮廓作为输入来模拟预测垂直轮/轨力的分析乘客轨道车辆的分析乘客轨道车辆的方法。使用分析系统研究神经建模方法显示来自结果的最明确的观察，这提供了该工具的潜在用户的理解，了解建模方法的期望和局限性。

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