Approximate Gaussian kernel mapping incremental LSSVR based on knowledge transfer and its industrial application

Abstract

Aiming at the problem of diversified and time-varying data in industrial energy forecast, an incremental least squares support vector regression model based on approximate Gaussian kernel mapping and knowledge transfer is proposed. Firstly, an incremental learning method based on historical parameter transfer is designed, which is applied to single output and multiple output least squares support vector regression models respectively. It can update the prediction model quickly and meet the requirements of real-time. In order to further improve the prediction accuracy, the approximate Gaussian kernel mapping based on Taylor series theory is used to extend the proposed model to the nonlinear case, which effectively solves the problem of kernel matrix storage. At the same time, the training process is optimized through Sherman–Morrison–Woodbury (SMW) formula, which improves the solution efficiency in enhancing the flexibility of the model. Experiments on industrial energy data show that the proposed method can improve the adaptive ability and prediction accuracy of the model, and improve energy efficiency.