EEMD Ensemble Empirical Mode Decomposition + ANN Neural Network Classification

EEMD (Ensemble Empirical Mode Decomposition) is an enhanced empirical mode decomposition method that effectively resolves the mode mixing problem in traditional EMD through noise-assisted analysis. This technique decomposes complex non-stationary signals into multiple Intrinsic Mode Functions (IMFs), where each IMF component represents local signal characteristics at different time scales. In implementation, EEMD typically requires setting noise amplitude and ensemble size parameters, with common practice involving multiple decomposition iterations with added white noise followed by ensemble averaging.

ANN (Artificial Neural Network) is a computational model mimicking biological neural networks' structure and functionality, exhibiting strong nonlinear mapping capabilities and self-learning characteristics. By constructing network architectures like multilayer perceptrons, ANN can perform complex pattern classification and prediction tasks. Key implementation aspects include selecting appropriate activation functions (e.g., ReLU, sigmoid), determining hidden layer dimensions, and employing backpropagation algorithms for weight optimization through gradient descent methods.

When combining EEMD with ANN for signal classification tasks, the standard workflow involves: First, applying EEMD to decompose raw signals into IMF components; Second, extracting time-domain, frequency-domain, or nonlinear features from these components as classification basis - common feature extraction methods include calculating IMF energy entropy, spectral characteristics, or Hurst exponents; Finally, feeding these features into ANN models for training and classification, where data normalization and cross-validation techniques are crucial for model generalization.

This hybrid approach demonstrates advantages in mechanical fault diagnosis and biomedical signal processing applications: EEMD provides more reliable signal decomposition results while ANN leverages its strong capabilities in complex pattern recognition. Critical considerations include IMF feature selection strategies and ANN architecture optimization, where parameter configurations like IMF component quantity, feature dimension reduction, and network topology must be experimentally determined through techniques such as grid search or genetic algorithms to achieve optimal classification performance.