Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model

Li, Guohui; Chang, Wanni; Yang, Huirong

doi:10.1155/2020/8385416

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…It is assumed that changes in instantaneous amplitude and IF ω k (t) = ϕ ′ k (t) are much smaller than phase. Based on the above assumptions, the input signal is assumed by VMD to be composed of a finite number of IMF with limited bandwidth and different central frequencies [24]. Under the constraint that the sum of each IMF component is approximately equal to the input signal, we iteratively seek the minimum sum of the estimated bandwidth of each component for each component synchronously.…”

Section: Vmd Algorithmmentioning

confidence: 99%

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Xie

Zhou

Liu

2023

Meas. Sci. Technol.

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Because the working conditions of the rotate vector (RV) reducer are often reciprocating variable speed and the working environment is relatively complex, the vibration signals collected have non-stationary vibration and complex environmental noise. A fault diagnosis method for RV reducer (WDT-IF-SS-VMD) is proposed, which combines instantaneous frequency (IF) trend graph based on current signal and parameter adaptive variational mode decomposition (VMD) algorithm. Firstly, the current signal and vibration signal are collected synchronously, and the steady phase of vibration signal is intercepted based on the instantaneous frequency trend diagram of current signal. Secondly, the intercepted vibration signals were transformed into scale space (SS), and the parameters of VMD were selected adaptively based and FCM. Then, the maximum kurtosis criterion is used to complete the extraction of sensitive components. Finally, the envelope analysis is carried out to complete the fault diagnosis. The measured signal analysis results show that this method can effectively separate and extract RV reducer fault features.

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Section: Vmd Algorithmmentioning

confidence: 99%

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Xie

Zhou

Liu

2023

Meas. Sci. Technol.

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“…The time-series decomposition (abbreviated by TSD) analysis seems to be an effective method to handle this problem [13] . In recent years, some TSD methods, such as: empirical mode decomposition (EMD) [14][15][16] , local mean decomposition (LMD) [17] , variational mode decomposition (VMD) [18][19][20] , etc. have caught many scholar's attentions.…”

Section: A Backgroundmentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

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Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.

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“…The adaptive volterra algorithm can automatically adjust its parameters according to the input signal [43], [44…”

Section: Volterra Modelmentioning

confidence: 99%

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

Chang

Yang

2020

IEEE Access

Self Cite

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Precipitation is an important parameter of water resource management, flood warning and hydrological analysis, so it is important to predict rainfall accurately. However, many previous studies did not extract the information of error series and only used a single model to predict rainfall data, ignoring the importance of model stability. Therefore, based on the idea of combination prediction and error correction strategy, this paper proposes a novel combined prediction model for monthly mean precipitation. It combines the variational mode decomposition (VMD), the improved butterfly optimization algorithm (IBOA), the least squares support vector machine model (LSSVM), the adaptive Volterra and autoregressive moving average (ARMA) model. Firstly, in order to find the best parameters of LSSVM, an improved butterfly optimization algorithm is proposed. The simulation results show the performance of IBOA is better than that of other algorithms, such as PSO, DE and BOA. Then the IBOA-LSSVM model and Volterra model are established for the mode components of the VMD, named VMD-IBOA-LSSVM and VMD-Volterra. Secondly, to solve the problem that the uncertainty of the hydrological prediction model, a combined precipitation prediction method based on the induced ordered weighted average (IOWA) operator of VMD-IBOA-LSSVM and VMD-Volterra is proposed. Finally, the ARMA model is established to correct the error sequence of the combined forecasting model. The precipitation data of two stations in Shaanxi Province are predicted. Experiment 1 is taken as an example, the maximum error of the proposed prediction model for rainfall is less than 9 mm, and the performance of the proposed model is improved by at least 43%. It shows that the proposed model can effectively reduce the prediction error of precipitation, and provide a new idea for precipitation prediction.

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Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model

Cited by 6 publications

References 29 publications

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

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