RISK ASSESSMENT IN ELECTRICAL POWER NETWORK PLANNING BASED ON SPARSE LEAST SQUARES SUPPORT VECTOR MACHINES

摘要：

To assess risk of electrical power network planning in an effective and fast way, the forecasting model of least square support vector machine (LS-SVM) based on pruning algorithm is established. Relative to the classical SVM, the least square SVM (LS-SVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. But sparseness is lost in the LS-SVM case. And the pruning algorithm make LSSVM recur sparseness. For illustration, a real-world planning project dataset is used to test the effectiveness of sparse least squares support vector machines(S-LS-SVM).

关键词： Electrical Power Network Planning Support Vector Machine Least Square Support Vector Machines Sparse Pruning Algorithm

作者: WEI SUN YUE MA

作者单位: Department of Economy Management, North China Electric Power University, Baoding 071003, Hebei, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1410-1414

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）