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首页> 外文会议>American Society of Mechanical Engineers(ASME) Rail Transportation Division Fall Conference; 20070911-12; Chicago,IL(US) >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：基于时延神经网络的时间序列测量中的车辆建模方法

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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：这项研究的主要目标是开发和演示一种通用的，有效的，基于数学和理论的，基于时间序列测量来建模非线性强迫振动机械系统的方法。提出了一种基于动力系统理论和非线性时间序列分析的强制动力系统系统辨识建模方法，该方法采用相空间重构（延时矢量嵌入）技术，通过时延神经网络（TDNN）对时序数据进行动力系统建模。这项工作的第一部分详细介绍了建模方法，包括动态系统背景，相空间重构和神经网络。在这项工作的第二部分中，基于对选定的分析集总参数强迫振动动力系统进行建模的能力对方法进行了评估，其中包括使用位移强迫函数预测集总质量位移的线性系统的示例。该工作讨论了在非线性系统中的应用，多自由度系统和多输入系统。进一步评估了该方法的能力，该方法可以使用垂直轨道轮廓作为输入来模拟预测垂直车轮/轨道力的分析型客运铁路车辆的能力。使用分析系统研究神经建模方法可从结果中获得最清晰的观察结果，这使该工具的潜在用户可以了解建模方法的期望和局限性。

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