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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

机译：基于高斯拉普拉斯混合噪声功能的双重方形支持向量复频模型及其在风速预测中的应用

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摘要

In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

机译：在本文中，观察到一些现实世界应用中的噪声，例如风力预测和到达估计问题的方向，不满足单噪声分布，包括高斯分布和拉普拉斯分布，而是混合分布。因此，将双型超平面与最小二乘速度的快速速度支持向量回归（LS-SVR），然后引入高斯拉普拉斯混合噪声特征，一个名为Gauss-Laplace Twin最小二乘支持向量回归的新的回归噪声功能（GL- TLSSVR），用于复杂的噪音。随后，我们应用增强拉格朗日乘法器方法来解决所提出的模型。最后，我们将短期风速数据集应用于所提出的模型。该实验的结果证实了我们提出的模型的有效性。

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期刊名称 Entropy
作者
Shiguang Zhang; Chao Liu; Wei Wang; Baofang Chang;
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年(卷),期 2020(22),10
年度 2020
页码 1102
总页数 18
原文格式 PDF
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机译：高斯拉普拉斯混合噪声;最小二乘支持向量回归;双超超平面;风速预测;

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专利

1. Noise model based v-support vector regression with its application to short-term wind speed forecasting [J] . Qinghua Hu, Shiguang Zhang, Zongxia Xie, Neural Networks: The Official Journal of the International Neural Network Society . 2014,第Null期

机译：基于噪声模型的v-支持向量回归及其在短期风速预测中的应用
2. A Parameter Self-Adjusting Optimization Model to Simulate and Forecast Short-Term Wind Speed Based on Least Square Support Vector Machine Regression Algorithm and Parameter Cross Validation Method [J] . Zhibin Liu, Qian Li Advanced Science Letters . 2012,第Null期

机译：基于最小二乘支持向量机回归算法和参数交叉验证法的短期风速参数自适应预测优化模型
3. Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model [J] . LI Li-min, CHENG Shao-kang, WEN Zong-zhou 山地科学学报（英文版） . 2021,第008期

机译：基于改进的主成分分析和混合核功能最小二乘支持向量回归模型的滑坡预测
4. Wind Speed and Direction Predictions Based on Multidimensional Support Vector Regression with Data-Dependent Kernel [C] . Dingcheng Wang, Yujia Ni, Beijing Chen, International Conference on Cloud Computing and Security . 2015

机译：基于数据相关核的多维支持向量回归的风速和风向预测
5. Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models [D] . Al-Ghraibah, Amani 2015

机译：支持向量回归的特征选择，火炬大小和火炬发射时间预测，以及基于时空测度的隐马尔可夫模型自动预测火炬行为
6. ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction [O] . Shiguang Zhang, Ting Zhou, Lin Sun, 2019

机译：基于高斯拉普拉斯混合噪声特性的风速预测噪声特性
7. Study of Learning Data Selection Method with K-nearest Neighbor and VAR Model in Wind Speed Prediction using Wind Speed Time-series Data of Multiple Sites by Support Vector Regression [O] . Aimi YUYAMA, Junya YAMASAKI, Daisuke MIZUNO, 2017

机译：基于多网站的风速预测与k最近邻和VAR模型的学习数据选择方法研究了传染媒介回归的风速时间序列数据

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

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