Journal ArticleDOI
Variational Mode Decomposition
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TLDR
This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.Abstract:
During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.read more
Citations
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Journal ArticleDOI
Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm
TL;DR: This investigation proposes a simple method to help those out-bounded cuckoo birds return to their previous (the most recent iteration) optimal location, by hybridizing the VMD method, the SVR model with the self-recurrent mechanism, the chaotic mapping function, the out-bound-back mechanism, and the cuckoos search algorithm.
Journal ArticleDOI
Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads
Zichen Zhang,Wei-Chiang Hong +1 more
TL;DR: In this article, a support vector regression (SVR) based electric load forecasting model was proposed by hybridizing variational mode decomposition (VMD), chaotic mapping mechanism, and grey wolf optimizer (GWO) in the VMD-SVR-CGWO model to improve the solution searching experiences and to determine the appropriate combination of SVRs parameters that improve forecasting accuracy.
Journal ArticleDOI
Subject independent emotion recognition from EEG using VMD and deep learning
Pallavi Pandey,K. R. Seeja +1 more
TL;DR: A subject independent emotion recognition technique is proposed from EEG signals using Variational Mode Decomposition (VMD) as a feature extraction technique and Deep Neural Network as the classifier that performs better compared to the state of the art techniques in subject-independent emotion recognition from EEG.
Journal ArticleDOI
Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition
Abhay Upadhyay,Ram Bilas Pachori +1 more
TL;DR: Experimental results at various signal to noise ratios (SNRs) are included in order to show the effectiveness of the proposed method compared to the other existing methods for V/NV detection in speech signals.
Journal ArticleDOI
Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm
Azim Heydari,Meysam Majidi Nezhad,Elmira Pirshayan,Davide Astiaso Garcia,Farshid Keynia,Livio de Santoli +5 more
TL;DR: A new and accurate combined model has been proposed for short-term load forecasting and short- term price forecasting in deregulated power markets that includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Journal ArticleDOI
Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
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