Successive multivariate variational mode decomposition

Liu, Shuaishuai; Yu, Kaiping

doi:10.1007/s11045-022-00828-w

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…Unlike VMD, the SVMD method does not require the precise number of mode components to be specified during signal processing. Instead, a continuous approach is employed to identify and extract all components effectively and accurately [43]. This approach holds a substantial role in enhancing the convergence rate while simultaneously reducing computational time.…”

Section: B Svmdmentioning

confidence: 99%

Hospital Outpatient Volume Prediction Model Based on Gated Recurrent Unit Optimized by the Modified Cheetah Optimizer

Keyimu,

Tuerxun,

Feng

et al. 2023

IEEE Access

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Precise outpatient volume prediction holds significant importance in hospital management. While the Gated Recurrent Unit (GRU) is a frequently utilized deep learning technique for forecasting hospital outpatient volumes, creating a proficient GRU model necessitates the fine-tuning of pertinent GRU parametersThe adjustment of suchparameters relies heavily on an individual's practical experience and prior knowledge. The recently proposed Cheetah optimizer is a novel intelligent algorithm with unique optimization capabilities. The Cheetah optimizer holds significant research potential; however, additional investigations are warranted, as it may be vulnerable to issues related to local optimization. In the present study, the selection of hyperparameters for the GRU model wasoptimized through the utilization of the Modified Cheetah Optimization (MCO) algorithm, and a combined MCO-GRU model was established. Using the Successive Variational Mode Decomposition (SVMD) method to decompose outpatient volume sample data, the parameters of the GRU model were optimized with the MCO method to construct a hybrid forecasting model. This yielded the smallest Root Mean Square Error (RMSE) for the proposed model, with a value of 0.0843. Additionally, the results indicate that in comparison to SVMD, Long Short-Term Memory (LSTM), GRU, Particle Swarm Optimization-GRU (PSO-GRU), and Cheetah Optimization-GRU (CO-GRU), the proposed model significantly enhanced the accuracy of outpatient volume forecasting. INDEX TERMSoutpatient volume prediction; GRU; MCO; SVMD; parameter optimization ABBREVIATIONS CO Cheetah Optimizer SARIMA Seasonal Autoregressive Integrated Moving Average model CC Correlation coefficient PSO-GRU Gated recurrent units model optimized by particle swarm optimization DBN Deep Belief Network CO-GRU Gated recurrent units model optimized by cheetah optimizer EMD Empirical Mode Decomposition MCO-GRU Gated recurrent units model optimized by modified Cheetah optimizer ETS Error Trend Seasonality AI Artificial Intelligence GA Genetic Algorithm ANN Artificial Neural Network GM Gray Model ARIMA Autoregressive Integrated Moving Average model GRU Gated Recurrent Unit network Average Average value of the algorithm GWO Grey Wolf Optimizer Best Best value of the algorithm HW Holt-Winters model The associate editor coordinating the review of this manuscript and approving it for publication was ***** IMF Intrinsic Mode Functions LSTM Long Short Term Memory

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Section: B Svmdmentioning

confidence: 99%

Hospital Outpatient Volume Prediction Model Based on Gated Recurrent Unit Optimized by the Modified Cheetah Optimizer

Keyimu,

Tuerxun,

Feng

et al. 2023

IEEE Access

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“…However, for complex signals with many modulated components, manually selecting them is not easy. In this case, one can turn to the successive mode extraction scheme of VMD (Nazari and Sakhaei 2020) or MVMD (Liu and Yu 2022), which can adaptively decompose modes like EMD.…”

Section: Theoretical Background and Derivation Of The Proposed Approachmentioning

confidence: 99%

A novel instantaneous frequency estimation method for operational time-varying systems using short-time multivariate variational mode decomposition

Liu

Zhao

et al. 2022

Journal of Vibration and Control

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Operational modal identification of time-varying systems plays a crucial role in assessing the health condition and controlling the dynamic properties of engineering structures. However, only the response is measurable, making it challenging. Based on the variational mode decomposition (VMD) theory, this paper presents a short-time multivariate or multi-channel VMD (STMVMD) method for instantaneous frequency (IF) identification of time-varying structures in the case of output-only measurements. The idea of short-time windows overcomes the shortcoming of many VMD-based methods that employ the narrowband assumption of intrinsic mode functions (IMFs) and cannot decompose non-stationary signals involving closely-spaced wideband IMFs. After obtaining the multivariate IMFs by STMVMD, an average scheme is employed to estimate IFs, reducing the noise sensitivity of Hilbert Transform. Moreover, by tracking the center frequencies of STMVMD at different moments, another more noise-robust IF estimation method is also presented. A series of numerical and experimental examples illustrate the advantages of the proposal.

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“…The VMD behavior as a filter bank was investigated in [ 74 ]. In order to increase the performance of the VMD, improved versions of the VMD were also proposed [ 75 , 76 , 77 ].…”

Section: Permutation Entropy—review Of Existing Theorymentioning

confidence: 99%

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy

Jabloun

Ravier

Buttelli

2022

Entropy

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Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.

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Successive multivariate variational mode decomposition

Cited by 12 publications

References 31 publications

Hospital Outpatient Volume Prediction Model Based on Gated Recurrent Unit Optimized by the Modified Cheetah Optimizer

Hospital Outpatient Volume Prediction Model Based on Gated Recurrent Unit Optimized by the Modified Cheetah Optimizer

A novel instantaneous frequency estimation method for operational time-varying systems using short-time multivariate variational mode decomposition

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy

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