Detecting changes in global dynamics with principal curves and information theory
Abstract (Summary)Two approaches to characterize global dynamics are developed in this dissertation. In particular, the concern is with nonlinear and chaotic time series obtained from physical systems. The objective is to identify the features that adequately characterize a time series, and can consequently be used for fault diagnosis and process monitoring, and for improved control. This study has two parts. The first part is concerned with obtaining a skeletal description of the data using Cluster-linked principal curves (CLPC).
School Location:USA - Tennessee
Source Type:Master's Thesis
Date of Publication: