# American Institute of Mathematical Sciences

doi: 10.3934/jimo.2019098

## Orthogonal intrinsic mode functions via optimization approach

 Faculty of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

* Corresponding author: Bingo Wing-Kuen Ling

Received  January 2019 Revised  March 2019 Published  July 2019

Fund Project: The first author is supported by NSF grant xx-xxxx

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.

Citation: Xinpeng Wang, Bingo Wing-Kuen Ling, Wei-Chao Kuang, Zhijing Yang. Orthogonal intrinsic mode functions via optimization approach. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2019098
##### References:

show all references

##### References:
Original signal
Signal components
Normalized IMFs
Components obtained by the Gram Schmidt orthogonalization method
Components obtained by our proposed method
Orthogonal errors of all the IMFs $err_{orth}$
 Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method $x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t) 0 0 1.3545  Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t)$ 0 0 1.3545
Errors between the total number of the extrema and the total number of the zero crossing points of all the IMFs $err_{ex_-zx}$
 Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method $x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t) 0 25 0  Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t)$ 0 25 0
Errors between the total number of the extrema and the total number of the zero crossing points of all the IMFs $err_{ex_-zx}$
 Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method $x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t) 0.9139\times10^{-4} 0.9801\times10^{-4} 0.6368\times10^{-4}  Signal Our proposed method Gram Schmidt orthogonalization method [8] Normalization method x(t)=\sin (2 \pi t) e^{-\frac{t^{2}}{2}}$$+e^{-t} \cos (2 \pi t)$ $0.9139\times10^{-4}$ $0.9801\times10^{-4}$ $0.6368\times10^{-4}$
 [1] A. M. Bagirov, Moumita Ghosh, Dean Webb. A derivative-free method for linearly constrained nonsmooth optimization. Journal of Industrial & Management Optimization, 2006, 2 (3) : 319-338. doi: 10.3934/jimo.2006.2.319 [2] Siwei Yu, Jianwei Ma, Stanley Osher. Geometric mode decomposition. Inverse Problems & Imaging, 2018, 12 (4) : 831-852. doi: 10.3934/ipi.2018035 [3] K. T. Arasu, Manil T. Mohan. Optimization problems with orthogonal matrix constraints. Numerical Algebra, Control & Optimization, 2018, 8 (4) : 413-440. doi: 10.3934/naco.2018026 [4] Mohamed Aly Tawhid. Nonsmooth generalized complementarity as unconstrained optimization. Journal of Industrial & Management Optimization, 2010, 6 (2) : 411-423. doi: 10.3934/jimo.2010.6.411 [5] René Henrion. Gradient estimates for Gaussian distribution functions: application to probabilistically constrained optimization problems. Numerical Algebra, Control & Optimization, 2012, 2 (4) : 655-668. doi: 10.3934/naco.2012.2.655 [6] Ahmet Sahiner, Gulden Kapusuz, Nurullah Yilmaz. A new smoothing approach to exact penalty functions for inequality constrained optimization problems. Numerical Algebra, Control & Optimization, 2016, 6 (2) : 161-173. doi: 10.3934/naco.2016006 [7] Xueting Cui, Xiaoling Sun, Dan Sha. An empirical study on discrete optimization models for portfolio selection. Journal of Industrial & Management Optimization, 2009, 5 (1) : 33-46. doi: 10.3934/jimo.2009.5.33 [8] Jinying Ma, Honglei Xu. Empirical analysis and optimization of capital structure adjustment. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-11. doi: 10.3934/jimo.2018191 [9] Juan Carlos De los Reyes, Carola-Bibiane Schönlieb. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization. Inverse Problems & Imaging, 2013, 7 (4) : 1183-1214. doi: 10.3934/ipi.2013.7.1183 [10] Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, J. Nathan Kutz. On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 2014, 1 (2) : 391-421. doi: 10.3934/jcd.2014.1.391 [11] Steven L. Brunton, Joshua L. Proctor, Jonathan H. Tu, J. Nathan Kutz. Compressed sensing and dynamic mode decomposition. Journal of Computational Dynamics, 2015, 2 (2) : 165-191. doi: 10.3934/jcd.2015002 [12] Hao Zhang, Scott T. M. Dawson, Clarence W. Rowley, Eric A. Deem, Louis N. Cattafesta. Evaluating the accuracy of the dynamic mode decomposition. Journal of Computational Dynamics, 2019, 0 (0) : 0-0. doi: 10.3934/jcd.2020002 [13] Xiaodi Bai, Xiaojin Zheng, Xiaoling Sun. A survey on probabilistically constrained optimization problems. Numerical Algebra, Control & Optimization, 2012, 2 (4) : 767-778. doi: 10.3934/naco.2012.2.767 [14] Chunrong Chen, T. C. Edwin Cheng, Shengji Li, Xiaoqi Yang. Nonlinear augmented Lagrangian for nonconvex multiobjective optimization. Journal of Industrial & Management Optimization, 2011, 7 (1) : 157-174. doi: 10.3934/jimo.2011.7.157 [15] Rakesh Pilkar, Erik M. Bollt, Charles Robinson. Empirical mode decomposition/Hilbert transform analysis of postural responses to small amplitude anterior-posterior sinusoidal translations of varying frequencies. Mathematical Biosciences & Engineering, 2011, 8 (4) : 1085-1097. doi: 10.3934/mbe.2011.8.1085 [16] Giancarlo Bigi. Componentwise versus global approaches to nonsmooth multiobjective optimization. Journal of Industrial & Management Optimization, 2005, 1 (1) : 21-32. doi: 10.3934/jimo.2005.1.21 [17] Nobuko Sagara, Masao Fukushima. trust region method for nonsmooth convex optimization. Journal of Industrial & Management Optimization, 2005, 1 (2) : 171-180. doi: 10.3934/jimo.2005.1.171 [18] Ian D. Morris. Ergodic optimization for generic continuous functions. Discrete & Continuous Dynamical Systems - A, 2010, 27 (1) : 383-388. doi: 10.3934/dcds.2010.27.383 [19] Caglar S. Aksezer. On the sensitivity of desirability functions for multiresponse optimization. Journal of Industrial & Management Optimization, 2008, 4 (4) : 685-696. doi: 10.3934/jimo.2008.4.685 [20] Cristian Barbarosie, Anca-Maria Toader, Sérgio Lopes. A gradient-type algorithm for constrained optimization with application to microstructure optimization. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 0-0. doi: 10.3934/dcdsb.2019249

2018 Impact Factor: 1.025