Detection-oriented Identification of Nonlinear Systems Using The NARMAX Model in Neural Networks

使用神经网络中的 NARMAX 模型进行面向检测的非线性系统辨识

• 批准号: 9753084
• 负责人: Fahmida Chowdhury
• 金额: $ 7.49万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-12-15 至 2000-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9753084&HistoricalAwards=false
• 关键词: Detection; oriented; Identification; Nonlinear; Systems

This proposal is for the enhancement of research/education al activities of the PI who is moving from Michigan Technological University (MTU) to the University of Southwestern Louisiana (USL) due to family reasons. The major educational activity will be the development of interdisciplinary courses. The first step toward this has already been taken: the PI developed and taught a graduate-level interdisciplinary course on Neural Networks at MTU. The next plan is to: (1) develop an undergraduate version of this course, (2) create a web-based tutorial for this course, (3) share the tutorial and the course outline and other materials with students and faculty nationwide. The undergraduate course on Neural Networks would be developed for a broad spectrum of students (all engineering and computer science majors). After the initial experimental phase with this course is over, the PI has the long-term plan to write an appropriate textbook for it. The technical research will involve a graduate student and an undergraduate student, and focus on the use of neural networks for the implementation of NARMAX a models for nonlinear system identification. Neural networks have been used for modeling and system identification by other researchers, but usually with the goal of control rather than fault detection. The main goal of the proposed research is to use recurrent neural networks (with successive degrees of refinement) to carry out system identification of the specific purpose of generating residuals that can be used to design efficient schemes for detection, isolation, and diagnosis of faults.

这项建议是为了加强由于家庭原因从密歇根理工大学（MTU）搬到西南路易斯安那大学（USL）的PI的研究/教育活动。主要的教育活动将是开发跨学科课程。实现这一目标的第一步已经迈出：PI在MTU开发并教授了一门研究生水平的跨学科神经网络课程。接下来的计划是：(1)开发这门课程的本科版本，(2)为这门课程创建一个基于网络的教程，(3)与全国的学生和教师分享教程、课程大纲和其他材料。神经网络的本科课程将面向广泛的学生（所有工程和计算机科学专业的学生）。在这门课程的初始实验阶段结束后，PI有一个长期的计划，为它写一本合适的教科书。技术研究将涉及一名研究生和一名本科生，重点是使用神经网络实现非线性系统识别的NARMAX模型。神经网络已被其他研究人员用于建模和系统识别，但通常以控制为目标，而不是故障检测。所提出的研究的主要目标是使用递归神经网络（具有连续的细化程度）来进行系统识别，以产生残差的特定目的，残差可用于设计有效的故障检测、隔离和诊断方案。