A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Tian, Zhongda; Li, Shujiang; Wang, Yanhong

doi:10.1002/we.2422

Cited by 97 publications

(61 citation statements)

References 51 publications

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“…In order to compare the goodness of fit of the least square method, the rational number approximation method without destroying the data distribution law is selected. There are several performance indicators added, such as root square derror (RMSE) [50], sum of squares due to error (SSE), R 2 and Adjusted R 2 [51]. Figure 10, Figure 11 and Figure 12 are the renderings after the two methods are fitted.…”

Section: B Melting Law Of Characteristic Parametersmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

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This paper focuses on how to perspective the melting behavior of solid iron tailings in molten blast furnace slag and take a new non-contact visual analytical method to predict its melting law. The optimized convolution neural network (CNN) is used to track the moving target in charge coupled device (CCD) camera system efficiently and accurately, and the melting behavior of SiO 2 is described by coordinate translation transformation theory. Hierarchical agglomerative clustering (HAC) and delaunay triangulation were used to extract the characteristic parameters of the melting process of SiO 2. The prediction model of the melting rate of SiO 2 at high temperature was established by least square fitting (LSF) and dimensional analysis, and compared with the actual melting rate of SiO 2 obtained by experiments. The results show that the melting characteristics of SiO 2 at high temperature are in accord with certain function rule. The performance of optimized CNN in terms of processing time and accuracy is significantly improved, and the fusion rate prediction model of SiO 2 is verified by 100% accuracy. It provides theoretical support and model basis for the improvement of slag cotton preparation technology. INDEX TERMS Melting rate, target tracking, feature extraction, dimensional analysis, best match.

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Section: B Melting Law Of Characteristic Parametersmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

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“…The reason why Reliability is introduced is to evaluate the Reliability of confidence intervals. The definition of these performance indicators are as the follows 60 :

RMSE = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(t_{i} - {\overset{t}{true}}_{i})}^{2}},

MAE = \frac{1}{N} \sum_{i = 1}^{N} ∣ t_{i} - {\overset{false¯}{t}}_{i} ∣,

MAPE = \frac{1}{N} \sum_{i = 1}^{N} ∣ \frac{t_{i} - {\overset{false¯}{t}}_{i}}{t_{i}} ∣ \times 100 %,

SSE = \sum_{i = 1}^{N} {(t_{i} - {\overset{t}{true}}_{i})}^{2},

TIC = \frac{\sqrt{\frac{1}{N} {((), t_{i} - {\overset{false¯}{t}}_{i})}^{2}}}{\sqrt{\frac{1}{N} \sum_{i = 1}^{N} {t_{i}}^{2}} + \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {\overset{t}{true}}_{i}^{2}}},

IA = 1 - \frac{\sum_{i = 1}^{N} {(t_{i} - {\overset{t}{true}}_{i})}^{2}}{}

…”

Section: Case Studiesmentioning

confidence: 99%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

Self Cite

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Summary Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.

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“…The feature vector of the FFT processed sequence was later used as the training sequence. In accordance with successful experience [55] and multiple attempts, in this paper, 60% of the data was used for training, 20% of data was used for testing, and 20% of data was used for validation. It was confirmed that the above proportion has the best training performance for the subsequent processes.…”

Section: Preparation Of Datamentioning

confidence: 99%

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

Ren

et al. 2020

Symmetry

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The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

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A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Cited by 97 publications

References 51 publications

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Network traffic prediction method based on wavelet transform and multiple models fusion

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

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