title: Representation of time-varying nonlinear systems with time-varying principal dynamic modes.
abstract: System identification of nonlinear time-varying (TV) systems has been a daunting task, as the number of parameters required for accurate identification is often larger than the number of data points available, and scales with the number of data points. Further, a 3-D graphical representation of TV second-order nonlinear dynamics without resorting to taking slices along one of the four axes has been a significant challenge to date. In this paper, we present a TV principal dynamic mode (TVPDM) method which overcomes these deficiencies. The TVPDM, by design, reduces one dimension, and by projecting PDM coefficients onto a set of basis functions, both nonstationary and nonlinear dynamics can be characterized. Another significant advantage of the TVPDM is its ability to discriminate the signal from noise dynamics, and provided that signal dynamics are orthogonal to each other, it has the capability to separate them. The efficacy of the proposed method is demonstrated with computer simulation examples comprised of various forms of nonstationarity and nonlinearity. The application of the TVPDM to the human heart rate and arterial blood pressure data during different postures is also presented and the results reveal significant nonstationarity even for short-term data recordings. The newly developed method has the potential to be a very useful tool for characterizing nonlinear TV systems, which has been a significant, challenging problem to date.
authors.affiliation: Department of Biomedical Engineering, State University of New York (SUNY) at Stony Brook, Stony Brook, NY 11794, USA. yuru.zhong@gmail.com
authors.forename: Yuru
authors.initials: Y
authors.lastname: Zhong
authors.forename: Kung-Ming
authors.initials: KM
authors.lastname: Jan
authors.forename: Ki H
authors.initials: KH
authors.lastname: Ju
authors.forename: Ki H
authors.initials: KH
authors.lastname: Chon
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D013997:Time Factors