"Nonlinear Systems Identification for Modelling and Analysis of Biological, Physical, and Industrial Processes"

“生物、物理和工业过程建模和分析的非线性系统识别”

• 批准号: 5985-2012
• 负责人: Korenberg, Michael
• 金额: $ 1.31万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568434
• 关键词: Nonlinear; Systems; Identification; Modelling; Analysis

Objectives of the proposed research are: (1) to develop techniques for nonlinear system identification and time-series analysis, creating methods capable of extracting useful information, e.g., significant frequency content, from short noisy experimental records. (2) to apply such methods to important problems in biology, and to physical and industrial systems. Scientific Approach: Regarding system identification, the proposed work involves representation of nonlinear systems by functional expansions and kernel estimation, parallel cascade identification (PCI) employing dynamic linear and static nonlinear elements, and use of other nonlinear models such as difference equations. Other means of representing nonlinear systems will continue to be actively explored. Regarding time-series analysis, proposed work includes estimation of sinusoidal, exponential, and exponentially-decaying sinusoidal series models from brief noisy time-series that may be unequally spaced. Regarding application of these methods in biology, the applicant intends to concentrate much activity on the interpretation of gene expression profiles and of tissue microarrays, such as for diagnosis and for prediction of treatment response. Physical and industrial applications will include reducing errors inherent in using (micro-electro-mechanical-system) MEMS-based inertial navigation systems (INS) and their fusion with a global positioning system (GPS).

拟议研究的目标是： (1) 开发非线性系统识别和时间序列分析技术，创建能够从短噪声实验记录中提取有用信息（例如重要频率内容）的方法。 (2) 将这些方法应用于生物学、物理和工业系统的重要问题。 科学方法： 关于系统识别，所提出的工作涉及非线性系统的表示 函数扩展和核估计、采用动态线性和静态非线性元件的并行级联识别（PCI）以及差分方程等其他非线性模型的使用。将继续积极探索表示非线性系统的其他方法。 关于时间序列分析，建议的工作包括估计正弦、指数和 来自可能不等间隔的短暂噪声时间序列的指数衰减正弦序列模型。 关于这些方法在生物学中的应用，申请人打算将大部分活动集中在基因表达谱和组织微阵列的解释上，例如用于诊断和预测治疗反应。物理和工业应用将包括减少使用（微机电系统）基于 MEMS 的惯性导航系统 (INS) 及其与全球定位系统 (GPS) 融合所固有的误差。