Signal prediction based on empirical mode decomposition and artificial neural networks

Abstract

In view of the usefulness of Empirical Mode Decomposition (EMD), Artificial Neural Networks (ANN), and Most Relevant Matching Extension (MRME) methods in dealing with nonlinear signals, we propose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.

Keywords

• EMD (Empirical Mode Decomposition)
• ANN (Artificial Neural Networks)
• MRME (Most Relevant Matching Extension)
• IMF (Intrinsic Mode Function)
• endpoint problem
• RBF (Radial Basis Function)