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Calibration transfer methods for feedforward neural network based instruments.

机译：基于前馈神经网络的仪器的校准传递方法。

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摘要

The calibration transfer problem examined by this thesis is that of attempting to exploit the knowledge of an initial instrument calibration model so as to obtain a second similar model, having acceptable accuracy, with less data than that used to obtain the initial model. This thesis considers instruments whose calibration model are based on a feedforward neural network, FFNN. The recalibration of these instruments has raised concerns regarding the significant quantity of data needed to perform their recalibration. Calibration transfer methods provide an alternative to recalibration which can reduce the data needed for a recalibration while maintaining acceptable levels of calibration accuracy.; Currently no reported methods of calibration transfer exist for the FFNN based instrument. This thesis develops a number of calibration transfer methods that allow a recalibration using less data than that needed in a recalibration employing conventional backpropagation learning. First, a simple non-learning method is introduced. A new method is developed based on a supervised learning algorithm employing a measure that learns the nth order partial derivatives of the desired calibration model provided by the calibration data. Finally, a simple unreported method of initialising the weights of a FFNN so as to begin learning from a point on the error surface that provides the approximation of a previously obtained calibration model is described.; Using computer simulations, the calibration error associated with using these calibration transfer methods are compared to the error obtained from a recalibration using conventional backpropagation learning. The simulations varied the numbers of neurons, number of calibration points, and similarity between calibration models. The desired calibration models were selected from 8th order polynomials and bandlimited normal random processes. The simulations indicated that no one method of calibration transfer provides the least calibration error but it is possible to achieve a 2 to 1000 fold decrease in the median calibration error relative to that of the standard recalibration while using half the calibration data. The results revealed that it is difficult to predict whether a specific set of calibration conditions will achieve a reduction in calibration error.
机译：本文所研究的标定传递问题是试图利用初始仪器标定模型的知识来获得第二个相似模型，该模型具有可接受的准确度，且数据量少于用于获得初始模型的模型。本文考虑其校准模型基于前馈神经网络FFNN的仪器。这些仪器的重新校准引起了人们对其执行重新校准所需的大量数据的关注。校准传递方法提供了重新校准的替代方法，可以减少重新校准所需的数据，同时保持可接受的校准精度水平。目前，基于FFNN的仪器尚无报告的校准转移方法。本论文开发了许多校准传递方法，这些方法允许比使用常规反向传播学习进行重新校准所需的数据更少的数据进行重新校准。首先，介绍一种简单的非学习方法。在监督学习算法的基础上开发了一种新方法，该方法采用一种方法来学习由校准数据提供的所需校准模型的 n 阶偏导数。最后，描述了一种简单的未报告的初始化FFNN权重的方法，以便从误差表面上的点开始学习，该点提供了先前获得的校准模型的近似值。使用计算机模拟，将与使用这些校准传递方法相关的校准误差与使用常规反向传播学习从重新校准获得的误差进行比较。模拟改变了神经元的数量，校准点的数量以及校准模型之间的相似性。从8阶多项式和带限正态随机过程中选择所需的校准模型。模拟表明，没有一种校准传递方法可以提供最小的校准误差，但是在使用一半校准数据的同时，相对于标准重新校准，中位数校准误差可以降低2到1000倍。结果表明，很难预测一组特定的校准条件是否会降低校准误差。

著录项

  • 作者

    Cibere, Joseph John.;

  • 作者单位

    The University of Saskatchewan (Canada).;

  • 授予单位 The University of Saskatchewan (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 258 p.
  • 总页数 258
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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