LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square [Formula: see text] of the Gaussian–Laplacian mixed homoscedastic ([Formula: see text]) and heteroscedastic noise ([Formula: see text]) for complicated or unknown noise distributions. The ALM technique is used to solve model [Formula: see text]. [Formula: see text] is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.