New Improved EMD Procedure (CEEMD)

CEEMD (Complementary Ensemble Empirical Mode Decomposition) is an improved EMD (Empirical Mode Decomposition) algorithm originally proposed by French researchers, primarily used in signal processing applications. This algorithm combines the advantages of EMD, EEMD (Ensemble Empirical Mode Decomposition), and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to enhance the stability and accuracy of modal decomposition.

The core concept of EMD algorithm involves decomposing complex signals into several Intrinsic Mode Functions (IMFs), but it often suffers from mode mixing issues. EEMD addresses this by introducing white noise to suppress mode mixing, though at the cost of higher computational complexity. CEEMD further optimizes this approach by incorporating complementary noise pairs, resulting in more robust decomposition outcomes. CEEMDAN enhances precision further by adaptively adjusting noise during the decomposition process.

Implementation typically involves iterative sifting processes with noise injection - for CEEMD, this means generating multiple realizations with positive and negative noise pairs and averaging the results. These algorithms find extensive applications in biomedical signal analysis, mechanical fault diagnosis, financial time series forecasting, and other domains. The improved CEEMD procedure usually includes sample code demonstrating signal decomposition using different methods, helping users quickly understand implementation approaches and select the most suitable algorithm based on specific requirements through comparative analysis of decomposition quality and computational efficiency.