CEEMDAN: An Enhanced Algorithm Improving Upon EMD and EEMD
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Resource Overview
CEEMDAN is an improved algorithm over EMD and EEMD, featuring subroutines and test examples in this package that are ready to run, with enhanced code implementation details for signal decomposition applications.
Detailed Documentation
CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) represents a significant improvement over both EMD (Empirical Mode Decomposition) and EEMD (Ensemble Empirical Mode Decomposition). This package includes comprehensive subroutines and test examples that can be executed directly. The algorithm's primary objective is to extract signal features by decomposing signals into a series of Intrinsic Mode Functions (IMFs). Through CEEMDAN implementation, which incorporates adaptive noise addition and ensemble averaging techniques, users can achieve more stable and effective analysis of complex signals. The package contains key functions for noise-assisted decomposition and mode extraction, providing researchers with robust tools for signal processing applications requiring high-resolution time-frequency analysis.
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