摘要
Adaptive data analysis provides an important tool in extracting hidden physical information from multiscale data that arise from various applications. In this paper, we review two data-driven time-frequency analysis methods that we introduced recently to study trend and instantaneous frequency of nonlinear and nonstationary data. These methods are inspired by the empirical mode decomposition method （EMD） and the recently developed compressed （compressive） sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form {a（t） cos（0（t））}, where a is assumed to be less oscillatory than cos（θ（t）） and θ ＇≥ 0. This problem can be formulated as a nonlinear ι0 optimization problem. We have proposed two methods to solve this nonlinear optimization problem. The first one is based on nonlinear basis pursuit and the second one is based on nonlinear matching pursuit. Convergence analysis has been carried out for the nonlinear matching pursuit method. Some numerical experiments are given to demonstrate the effectiveness of the proposed methods.
Adaptive data analysis provides an important tool in extracting hidden physical information from multiscale data that arise from various applications.In this paper,we review two data-driven time-frequency analysis methods that we introduced recently to study trend and instantaneous frequency of nonlinear and nonstationary data.These methods are inspired by the empirical mode decomposition method(EMD)and the recently developed compressed(compressive)sensing theory.The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form{a(t)cos(θ(t))},wherea is assumed to be less oscillatory than cos(θ(t))andθ0.This problem can be formulated as a nonlinear l0optimization problem.We have proposed two methods to solve this nonlinear optimization problem.The frst one is based on nonlinear basis pursuit and the second one is based on nonlinear matching pursuit.Convergence analysis has been carried out for the nonlinear matching pursuit method.Some numerical experiments are given to demonstrate the efectiveness of the proposed methods.
基金
supported by Air Force Ofce of Scientifc Research
Multidisciplinary University Research Initiative
USA(Grant No.FA9550-09-1-0613)
Department of Energy of USA(Grant No.DE-FG02-06ER25727)
Natural Science Foundation of USA(Grant No.DMS-0908546)
National Natural Science Foundation of China(Grant No.11201257)
