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A methodology for the modeling of forced dynamical systems from time series measurements using time-delay neural networks.

机译：一种使用时延神经网络从时间序列测量中对强迫动力系统建模的方法。

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摘要

The main goal of this research is to develop a general, efficient, mathematically and theoretically based methodology to model nonlinear forced vibrating mechanical systems from time series measurements. This dissertation details a novel system identification modeling methodology for dynamical systems based on dynamic system theory and nonlinear time series analysis that employs phase space reconstruction (delay vector embedding) and neural networks for modeling of dynamical systems from time series data using time-delay neural networks (TDNN). The first part of this dissertation details the development of the modeling methodology including background on dynamic systems, system identification, neural networks, and phase space reconstruction. A brief description of the modeling methodology is as follow: (1) A dynamic system is forced with a representative sampled input [x(t)] and a set of sample outputs [y(t)] is measured; (2) Output data, [y(t)], is then used to determine the phase space reconstruction parameters of embedding dimension and time lag, and an input/output delay vector for the dynamic system is defined; (3) Using the dynamic systems delay vector, the architecture of a time-delayed neural network with feedback from the output (predicted output, [ ŷ(t)]) is constructed and trained using a segment of the measured input-output data from Step 1; (4) The neural model is validated and evaluated for its ability to generalize the response of the dynamic system; and (5) The validated neural model is used to predict dynamic system response for a new input forcing.; 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 linear systems, 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 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：这项研究的主要目的是开发一种通用的，有效的，基于数学和理论的方法，以根据时间序列测量来建模非线性强迫振动机械系统。本文详细介绍了一种基于动力学系统理论和非线性时间序列分析的动力学系统系统辨识建模方法，该方法采用相空间重构（延迟矢量嵌入）和神经网络，通过时延神经网络对时序数据进行动力学系统建模。 （TDNN）。本文的第一部分详细介绍了建模方法的发展，包括动态系统背景，系统识别，神经网络和相空间重构。建模方法的简要描述如下：（1）用一个有代表性的采样输入[x（t）]强制一个动态系统，并测量一组采样输出[y（t）]； （2）然后，使用输出数据[y（t）]来确定嵌入维数和时滞的相空间重构参数，并定义动态系统的输入/输出延迟矢量； （3）使用动态系统延迟向量，使用测得的输入-输出数据的一部分来构造和训练带有输出反馈（预测输出，[＆ycirc（t）]）的时延神经网络的架构。从步骤1开始； （4）验证和评估神经模型具有概括动态系统响应的能力； （5）经过验证的神经模型用于预测新输入强迫的动态系统响应。在这项工作的第二部分中，基于对模型（包括线性系统，非线性系统，多自由度系统和多输入系统）的选定分析集总参数强迫振动动态系统进行建模的能力，对该方法进行了评估。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显示出从结果中最清晰的观察结果，这些结果使该工具的潜在用户可以了解建模方法的期望和局限性。

著录项

  • 作者

    Zolock, John D.;

  • 作者单位

    Tufts University.;

  • 授予单位 Tufts University.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 237 p.
  • 总页数 237
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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