Adaptive Rectification of Data from Nonlinear Dynamic Processes via Recursive Neural Networks

通过递归神经网络对非线性动态过程中的数据进行自适应校正

• 批准号: 9216380
• 负责人: David Himmelblau
• 金额: $ 15.38万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-10-15 至 1995-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216380&HistoricalAwards=false
• 关键词: Adaptive; Rectification; Data; Nonlinear; Dynamic

A problem in many areas of science and engineering is that of data rectification. For example, in a plant a wide variety of measurements are made of the process variables for purposes such as cost accounting, process control, statistical quality control, and performance evaluation. Not all of the possible variables are measured, some of the measurements are defective in the sense that gross errors creep into the measurements because of instrument degradation (or human error), noise exists, and unmeasured disturbances enter the processes. The objective of data rectification is to provide the best estimates of the process variables that satisfy the process model assuming the process model is an accurate one - which may or not be the case. If flawed information is used for process control or cost accounting, process performance can be degraded, perhaps substantially. Data rectification involves not only reduction of noise but removing of other types of contaminants in the data, particularly gross errors. A problem with some existing techniques for data rectification in dynamic processes is their inability to detect gross errors. Often a process model of a complex nonlinear process may be inadequate to represent the dynamics of a process. Model mismatch prevents correct detection of disturbances and outliers, and rectified variables that satisfy an erroneous model will include systematic errors. The use of artificial neural networks (ANN) for data rectification can ameliorate this problem because the mapping they accomplish can easily be of higher quality than that yielded by models based on physical principles or regression analysis. At the same time, an ANN could serve as a filter to damp out random noise and gross errors. This research involves the investigation of the possible uses of recursive ANN to implicitly model dynamic chemical process equipment with the goal of data rectification. The PI plans to generate deterministic simulations of simple and complex nonlinear dynamic processes (so that the true values of the process variables are known), and to these values add various kinds of random noise and gross errors to generate simulated measurements akin to real process data. Scaled simulated data will become input to various kinds of recursive ANN, and the outputs of the nets will be the rectified values of the variables. The effect of: (1) the magnitude and frequency of the gross errors, (2) the degree of auto and cross correlation in the noise, (3) model mismatch between the net and the true model, (4) missing measurements, (5) the sampling rate and delay in the measurements, and (6) the appropriate types of recursive nets will be evaluated.***//

在科学和工程的许多领域中的一个问题是 数据校正 例如，在一种植物中， 对过程变量进行测量， 如成本核算、过程控制、统计质量 控制和性能评估。 并非所有可能的 测量变量时，某些测量结果存在缺陷 在这个意义上说，粗差蠕变到测量 由于仪器退化（或人为错误）、噪声 存在，不可测量的干扰进入过程。 的 数据校正的目标是提供最好的 满足过程的过程变量的估计 模型假设流程模型是准确的， 可能是也可能不是。 如果有缺陷的信息被用于 过程控制或成本核算，过程绩效可以 退化了，也许是大幅退化了。 数据纠正涉及 不仅减少了噪音，而且消除了其他类型的噪音。 数据中的污染物，特别是严重错误。 一些现有的数据校正技术存在的问题 在动态过程中，他们无法检测到 错误. 通常是复杂非线性过程的过程模型 可能不足以表示过程的动态。 模型失配妨碍了正确检测干扰， 异常值和满足错误 模型将包含系统误差。 使用人工 用于数据校正的神经网络（ANN）可以改善 这个问题，因为他们完成的映射可以很容易地 比基于以下模型产生的质量更高 物理原理或回归分析。 与此同时， 人工神经网络可以作为过滤器来抑制随机噪声， 严重错误。 这项研究涉及调查可能的用途 递归神经网络隐式建模动态化工过程 以数据校正为目标的设备。 PI计划 生成简单和复杂的确定性模拟， 非线性动态过程（因此， 过程变量是已知的），并且向这些值添加各种 各种随机噪声和粗差生成模拟 测量类似于真实的过程数据。 比例模拟数据 将成为各种递归ANN的输入， 网络的输出将是 变量 影响： (1)粗差的幅度和频率， (2)噪声中自相关和互相关的程度， (3)网络和真实模型之间的模型失配， (4)缺少测量， (5)测量中的采样率和延迟，以及 (6)适当类型的递归网 将进行评估。*//