Explore MATLAB source code curated for "经验模态分解" with clean implementations, documentation, and examples.
Development of an EMD decomposition program based on the principles of Empirical Mode Decomposition to facilitate deeper understanding of decomposition mechanisms, analysis of methodological advantages, and identification of limitations
After applying Empirical Mode Decomposition, three-dimensional plots can be generated using instantaneous frequency, time, and amplitude as coordinates to visualize signal characteristics.
A functional MATLAB program that performs empirical mode decomposition on time series data and implements predictive modeling using least squares support vector machines (LS-SVM)
Full implementation of the 2005 version EMD (Empirical Mode Decomposition) program with comprehensive signal processing capabilities
2007 Edition EMD (Empirical Mode Decomposition) Complete Program with Algorithm Implementation Details
EMD method for nonlinear signal analysis within Hilbert-Huang Transform, focusing on fixed intrinsic mode functions and instantaneous frequency computation with algorithmic implementation insights.
Application Background: Empirical Mode Decomposition (EMD) is a time-frequency analysis method for processing nonlinear and non-stationary signals. This method adaptively decomposes input signals into several Intrinsic Mode Functions (IMFs) based on their inherent characteristics without requiring prior knowledge. It is widely used in signal denoising and non-stationary time series prediction. Key Technology: The EMD algorithm enables denoising, analysis, and prediction of high-frequency signals through decomposition and trend analysis. The MATLAB implementation typically involves iterative sifting processes, envelope detection using cubic spline interpolation, and stopping criteria based on standard deviation thresholds.
A MATLAB-based implementation of EMD (Empirical Mode Decomposition) routines featuring Hilbert-Huang Transform and empirical mode decomposition algorithms with practical application examples. This implementation demonstrates signal decomposition using the sifting process and Hilbert spectral analysis.
A data-driven decomposition approach similar to Empirical Mode Decomposition (EMD) that breaks down complex signals into several proper rotation components and a residual term. Implementation typically involves iterative signal processing algorithms to extract intrinsic mode functions through local extrema detection and sifting processes.
This program displays all Intrinsic Mode Functions (IMFs) and residual signals obtained through Empirical Mode Decomposition (EMD), along with the reconstructed signal from these components, featuring customizable decomposition parameters and signal processing capabilities.