• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Prediction interval estimation techniques for empirical modeling strategies and their applications to signal validation tasks.
【24h】

Prediction interval estimation techniques for empirical modeling strategies and their applications to signal validation tasks.

机译：用于经验建模策略的预测间隔估计技术及其在信号验证任务中的应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The basis of this work was to evaluate both parametric and non-parametric empirical modeling strategies applied to signal validation or on-line monitoring tasks. On-line monitoring methods assess signal channel performance to aid in making instrument calibration decisions, enabling the use of condition-based calibration schedules. The three non-linear empirical modeling strategies studied were: artificial neural networks (ANN), neural network partial least squares (NNPLS), and local polynomial regression (LPR).; The evaluation of the empirical modeling strategies includes the presentation and derivation of prediction intervals for each of three different model types studied. An estimate and its corresponding prediction interval contain the measurements with a specified certainty, usually 95%. The prediction interval estimates were compared to results obtained from bootstrapping via Monte Carlo resampling, to validate their expected accuracy.; Properly determined prediction interval estimates were obtained that consistently captured the uncertainty of the given model such that the level of certainty of the intervals closely matched the observed level of coverage of the prediction intervals over the measured values. In most cases the expected level of coverage of the measured values within the prediction intervals was 95%. The prediction intervals were required to perform adequately under conditions of model misspecification. The results also indicate that instrument channel drifts are identifiable by observing the drop in the level of coverage of the prediction intervals to relatively low values, e.g. 30%.; A comparative evaluation of the different empirical models was also performed. The evaluation considers the average estimation errors and the stability of the models under repeated Monte Carlo resampling. The results indicate the large uncertainty of ANN models applied to collinear data, and the utility of the NNPLS model for the same purpose. While the results from the LPR models remained consistent for data with or without collinearity, assuming proper regularization was applied.; All of the methods studied herein were applied to a simulated data set for an initial evaluation of the methods, and data from two different U.S. nuclear power plants for the purposes of signal validation for on-line monitoring tasks.
机译：这项工作的基础是评估应用于信号验证或在线监测任务的参数和非参数经验建模策略。在线监测方法评估信号通道的性能，以帮助做出仪器校准决定，从而可以使用基于条件的校准时间表。研究的三种非线性经验建模策略是：人工神经网络（ANN），神经网络偏最小二乘（NNPLS）和局部多项式回归（LPR）。对经验建模策略的评估包括所研究的三种不同模型类型中每种模型的预测间隔的表示和推导。估计及其相应的预测间隔包含具有确定确定性（通常为95％）的测量。将预测间隔估计值与通过蒙特卡洛重采样从自举获得的结果进行比较，以验证其预期精度。获得了正确确定的预测间隔估计值，该估计值一致地捕获了给定模型的不确定性，以使间隔的确定性水平与测量值上观察到的预测间隔的覆盖水平紧密匹配。在大多数情况下，预测间隔内测量值的预期覆盖率是95％。需要预测间隔才能在模型错误指定的条件下充分执行。结果还表明，通过观察预测间隔的覆盖水平下降到相对较低的值（例如， 30％。还对不同的经验模型进行了比较评估。评估考虑了平均估计误差和在重复蒙特卡洛重采样下模型的稳定性。结果表明，应用于共线数据的ANN模型存在较大的不确定性，并且NNPLS模型可用于相同目的。虽然LPR模型的结果对于具有或不具有共线性的数据保持一致，但假设应用了适当的正则化。本文研究的所有方法都应用于模拟数据集，用于方法的初始评估，以及来自两个不同美国核电厂的数据，用于在线监视任务的信号验证。

著录项

  • 作者

    Rasmussen, Brandon P.;

  • 作者单位

    The University of Tennessee.;

  • 授予单位 The University of Tennessee.;
  • 学科 Engineering Nuclear.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 366 p.
  • 总页数 366
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 原子能技术;人工智能理论;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号