Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Zhang, Shiguang; Liu, Chao; Wang, Wei; Chang, Baofang

doi:10.3390/e22101102

Cited by 11 publications

(6 citation statements)

References 58 publications

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“…At present, the social competition is fierce, the society is full of opportunities and challenges, and various factors such as employment difficulties also aggravate students' unhealthy psychological phenomena and mental disorders [ 1 ]. Students in school are the key growth stage of physiology and psychology, and some unhealthy activities fetter students' physical and mental health, career and self-realization, which also hinders the harmonious development of society [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Football Education Environment and Students’ Psychology Health Based on Gauss Characteristics

Qiao

Huang

2022

Journal of Environmental and Public Health

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Campus football has become a core content of school physical education. Through football education, we can cultivate students’ sound personality and promote students’ all-round physical and mental development. At the same time, through psychological skills training methods, we can enrich the educational methods of football skills and provide theoretical reference for promoting educational reform. On the basis of Gaussian features, this paper combines the mixed Gaussian feature model to further describe the relationship between football education and students’ psychology. At the same time, Apriori association rule algorithm in data mining is introduced, and Apriori algorithm is improved in parallel with Hadoop data processing platform. Several parallel association rule algorithms are emphatically studied and analyzed to strengthen the analysis of the relationship between football education and students’ psychology. The results show that the average recognition rate of the correlation between football education and students’ psychology based on Gaussian features is 17.91% higher than that of ordinary results, which obviously improves the correlation recognition result and has a good descriptive ability. Therefore, it has become an important issue for today’s physical education workers to analyze the correlation between football education and students’ psychology in order to cultivate students’ sports and mental health.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Football Education Environment and Students’ Psychology Health Based on Gauss Characteristics

Qiao

Huang

2022

Journal of Environmental and Public Health

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“…Step 5: update the explosion range and spark number of each group of hyperparameters according to formulas (8) and (9).…”

Section: Hyperparameter Optimization Of Lstm By the Fireworkmentioning

confidence: 99%

“…Intelligent learning methods, such as Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), train and predict the wind speed data with better performance in the fitting of the nonlinear changes of wind speed [5][6][7]. SVR, MLR, and DTR have advantages in sparsity and generalization and solving nonlinearity prediction problems, but its key parameters mainly rely on manual selection [8][9][10][11]. ANN [12,13] has the advantages of good nonlinear fitting and strong self-learning ability, but it is unstable, slow convergence rate is easy to fall into the local optimal value, and it is difficult to obtain its network structure including the number of hidden layers.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Shao

Song

Bian

et al. 2021

Advances in Materials Science and Engineering

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Wind energy is a renewable energy source with great development potential, and a reliable and accurate prediction of wind speed is the basis for the effective utilization of wind energy. Aiming at hyperparameter optimization in a combined forecasting method, a wind speed prediction model based on the long short-term memory (LSTM) neural network optimized by the firework algorithm (FWA) is proposed. Focusing on the real-time sudden change and dependence of wind speed data, a wind speed prediction model based on LSTM is established, and FWA is used to optimize the hyperparameters of the model so that the model can set parameters adaptively. Then, the optimized model is compared with the wind speed prediction based on other deep neural architectures and regression models in experiments, and the results show that the wind speed model based on FWA-improved LSTM reduces the prediction error when compared with other wind speed prediction-based regression methods and obtains higher prediction accuracy than other deep neural architectures.

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“…e loss function is not only related to partial support vector samples, but also to learn all samples, so as to correct the fitting error, which greatly improves the prediction accuracy. LSSVM transforms the quadratic convex optimization problem of the support vector machine into solving linear equations, which greatly reduces the difficulty of training [38][39][40][41][42][43]. References [41,42] discussed the use of the LSSVM model for power module fault prediction and short-term traffic flow prediction.…”

Section: Stone Cultural Relics Crack Predictionmentioning

confidence: 99%

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Liu

et al. 2021

Mathematical Problems in Engineering

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Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Cited by 11 publications

References 58 publications

[Retracted] Analysis of the Correlation between Football Education Environment and Students’ Psychology Health Based on Gauss Characteristics

[Retracted] Analysis of the Correlation between Football Education Environment and Students’ Psychology Health Based on Gauss Characteristics

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

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