Abstract
ABSTRACTThis paper proposes an improved ensemble empirical mode decomposition method based on genetic algorithm to solve the mode mixing problem in empirical mode decomposition (EMD) algorithm as well as the parameters selection issue in ensemble empirical mode decomposition (EEMD) algorithm. In a genetic algorithm (GA), the orthogonality index is used to formulate the fitness function and the Hamming distance is specified to design the difference selection operator. By coupling GA with EEMD algorithm, an improved decomposition method with higher efficiency is generated, namely GAEEMD. Simulation experiment with both intermittent signals and sinusoidal signals verifies the effectiveness and robustness of the proposed GAEEMD, compared with EMD, EEMD, and original GA algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
