A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

Li, Guohui; Ma, Xiao; Yang, Huirong

doi:10.1155/2018/3713410

Cited by 20 publications

(6 citation statements)

References 20 publications

(23 reference statements)

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“…So, the probability [ 22 , 24 ]

is the probability of passing 1st order from state i to state j by the ( n + 1)-th step. These probabilities do not depend on the time step n (stationary), i.e., [ 29 , 30 ]

so we have homogeneous chains { Xn }.…”

Section: Methodsmentioning

confidence: 99%

Privacy Leaks Protection in Music Streaming Services Using an Intelligent Permissions Management System

Wang

2022

Computational Intelligence and Neuroscience

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A security violation is referred to as a personal data breach when it leads to unintentional and unlawful destruction, loss, alteration, unauthorized disclosure, or access to personal data that has been communicated, stored, or otherwise processed in some other manner. Based on the principles of information security, we can define a breach of confidentiality as the unauthorized or accidental disclosure or access to personal data, a breach of integrity as the unauthorized or accidental alteration of personal data, and a breach of availability as the unauthorized or accidental loss of access to or destruction of personal data. This paper suggests designing an intelligent consensus policy management system based on the Markov chain approach. It is a novel system that would analyze the present status of the consensus elements for future development and anticipates the possibility of possible breaches of sensitive personal data. The evaluation of the proposed strategy is based on a policy scenario that involves a hypothetical consensus and a data breach of sensitive information to music streaming services.

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“…So, the probability [ 22 , 24 ]

is the probability of passing 1st order from state i to state j by the ( n + 1)-th step. These probabilities do not depend on the time step n (stationary), i.e., [ 29 , 30 ]

so we have homogeneous chains { Xn }.…”

Section: Methodsmentioning

confidence: 99%

Privacy Leaks Protection in Music Streaming Services Using an Intelligent Permissions Management System

Wang

2022

Computational Intelligence and Neuroscience

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“…In this paper, the prediction results of LSSVM and Volterra model are combined by IOWA operator, and each single prediction method is weighted by the order of fitting precision of each time point in the sample interval, and a new combined prediction method is established based on the sum of error squares. According to Equation (27), the IOWA combined prediction model is as follows: (35) The above formula is solved by Matlab optimization toolbox to get 1 2 1, 0 w w   , and the prediction value of the hybrid prediction model can be obtained by substituting the weight into Equation (26). The prediction results of each point obtained by combining the LSSVM model and Volterra model with IOWA operator is shown in Fig.…”

Section: Experiments I: the Analysis Of Site 1 Precipitation Predicmentioning

confidence: 99%

“…Wu et al [34] combined VMD with prediction method to forecast AQI . Li et al [35] proposed a forecasting model of sunspot number based on the combination of VMD and BP neural network. At present, most scholars use the combination of the mode decomposition and the single forecasting model to predict the rainfall data.…”

Section: Introductionmentioning

confidence: 99%

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

Chang

Yang

2020

IEEE Access

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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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“…Under the constraint that the sum of each IMF component is equal to the input signal x(t), the variational model of signal decomposition is constructed with the goal of minimizing the sum of estimated bandwidth of each IMF. e process of establishing the variational model is as follows [40]:…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

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

Chang

Yang

2020

Advances in Meteorology

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Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model.

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A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

Cited by 20 publications

References 20 publications

Privacy Leaks Protection in Music Streaming Services Using an Intelligent Permissions Management System

Privacy Leaks Protection in Music Streaming Services Using an Intelligent Permissions Management System

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

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

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