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

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，创建能够提取有用信息的方法，例如，显着的频率内容，从短期嘈杂的实验记录。 (2)将这些方法应用于生物学中的重要问题，以及物理和工业系统。 科学方法： 关于系统识别，建议的工作涉及非线性系统的表示， 函数展开和核估计，并行级联识别（PCI）采用动态线性和静态非线性元素，并使用其他非线性模型，如差分方程。表示非线性系统的其他方法将继续积极探索。 关于时间序列分析，建议的工作包括估计正弦，指数， 指数衰减的正弦序列模型从短暂的噪声时间序列，可能是不均匀的间隔。 关于这些方法在生物学中的应用，申请人打算将许多活动集中在基因表达谱和组织微阵列的解释上，例如用于诊断和用于预测治疗反应。物理和工业应用将包括减少使用（微机电系统）基于MEMS的惯性导航系统及其与全球定位系统的融合所固有的误差。

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).