An improved genetic algorithm for optimizing ensemble empirical mode decomposition method. Issue 2 (29th November 2019)

• Record Type: Journal Article
• Authors: Zhang, Dabin; Cai, Chaomin; Chen, Shanying; Ling, Liwen
• Is Part Of: Systems science & control engineering. Volume 7:Issue 2(2019)
• Page Start: 53
• Page End: 63
• Publication Date: 2019-11-29
• Subjects: Ensemble empirical mode decomposition -- mode mixing -- selection operator -- noise-assisted; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2019.1627598
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: This 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.