A New Approach for Nonlinear Signals Empirical Mode Decomposition

Huangs data-driven technique Empirical Mode Decomposition is presented and issues related to its effective implementation are discussed. This is nonlinear signal evolution processes. Presently available methods are either nonlinear or no stationary data analysis methods, our prime focus is on both nonlinear & no stationary. Empirical Mode Decomposition is here proposed as an intuitive, adaptive powerful tool for nonlinear signals. Its base function is derived from the signal itself i.e. adaptive method. This derived function represents physically meaningful instantaneous frequencies by using Hilbert spectrum especially for nonlinear and no stationary process. It provides more specific and real definition of particular events in time-frequency space than wavelet analysis as well as more physically meaningful interpretations of the underlying dynamic processes. Because of adaptive property, there is difficulty of laying a firm theoretical foundation & also present the mathematical issues associated with the new method.