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论文类型：期刊论文

发表时间：2022-06-28

发表刊物：系统仿真学报

所属单位：电子信息与电气工程学部

卷号：21

期号：8

页面范围：2152-2155,2194

ISSN号：1004-731X

摘要：Improving the linear ridge regression by using the kernel function which meets the Mercer's condition, a novel nonlinear kernel ridge regression based on directly optimization was proposed. Compared with the support vector machine regression, this method is characterized by computing the squared lose function and the number of the parameters in the kernel ridge regression that need for selection are reduced, moreover, the time of modeling can reduce using the directly optimization. The simulation results and the application results in the soft sensing of aviation kerosene show the effectiveness of this method.

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