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Since Huang proposed the Empirical Mode Decomposition (EMD) in 1998, modedecomposition has been widely studied, but EMD and relative developedalgorithms are still generally lack of adaptability and mathematical theory.This paper propose a new mode decomposition algorithm called Neural ModeDecomposition (NMD) based on Fourier neural network (FNN) and frequencyclustering. Firstly, a FNN is constructed to decompose and learn theinformation of each amplitude modulation frequency component and non-periodiccomponent in the raw data. Secondly, the frequency components obtained by theFNN are clustered into multiple Intrinsic Mode Functions (IMF) with separatedspectrum based on the energy of each frequency component learned by FNN.Practical decomposition results on a series of artificial and real data showthat NMD algorithm can effectively implement mode decomposition, better reflectthe characteristics of raw data than EMD, and has higher adaptability thanVariational Mode Decomposition (VMD).

Neural Mode Decomposition based on Fourier neural network and frequency clustering