Orthogonal intrinsic mode functions via optimization approach

• Published in: Journal of industrial and management optimization Vol. 17; no. 1; pp. 51 - 66
• Main Authors: Wang, Xinpeng, Ling, Bingo Wing-Kuen, Kuang, Wei-Chao, Yang, Zhijing
• Format: Journal Article
• Language: English
• Published: Springfield American Institute of Mathematical Sciences 01.01.2021
• Subjects: Lagrange multiplier; Matrix; Optimization; Studies; Empirical mode decomposition; nonconvex optimization; exclusive or constrained optimization; nonsmooth optimization; orthogonal intrinsic mode functions
• ISSN: 1553-166X; 1547-5816; 1553-166X
• DOI: 10.3934/jimo.2019098

This paper proposes an optimization approach to find a set of orthogonal intrinsic mode functions (IMFs). In particular, an optimization problem is formulated in such a way that the total energy of the difference between the original IMFs and the corresponding obtained IMFs is minimized subject to both the orthogonal condition and the IMF conditions. This formulated optimization problem consists of an exclusive or constraint. This exclusive or constraint is further reformulated to an inequality constraint. Using the Lagrange multiplier approach, it is required to solve a linear matrix equation, a quadratic matrix equation and a highly nonlinear matrix equation only dependent on the orthogonal IMFs as well as a nonlinear matrix equation dependent on both the orthogonal IMFs and the Lagrange multipliers. To solve these matrix equations, the first three equations are considered. First, a new optimization problem is formulated in such a way that the error energy of the highly nonlinear matrix equation is minimized subject to the linear matrix equation and the quadratic matrix equation. By finding the nearly global optimal solution of this newly formulated optimization problem and checking whether the objective functional value evaluated at the obtained solution is close to zero or not, the orthogonal IMFs are found. Finally, by substituting the obtained orthogonal IMFs to the last matrix equation, this last matrix equation reduced to a linear matrix equation which is only dependent on the Lagrange multipliers. Therefore, the Lagrange multipliers can be found. Consequently, the solution of the original optimization problem is found. By repeating these procedures with different initial conditions, a nearly global optimal solution is obtained.