School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, China)Abstract: The BF status evidential samples are limited and the approach based on empirical risk minimization principle doesn’t work well.SVM (support vector machine
Abstract：The BF status evidential samples are limited and the approach based on empirical risk minimization principle doesn’t work well.SVM (support vector machine) approach is aimed at solving classification problem with a small sample of training and has better ability for generalization,LSSVM approach is proposed to diagnose BF status. Finally the effectiveness of the approch was evaluated by MATLAB simulation.
曲飞;吴敏;曹卫华;何勇. 基于支持向量机的高炉炉况诊断方法[J]. 钢铁, 2007, 42(10): 0-0.
QU Fei;WU Min;CAO Weihua;HE Yong. BF Status Diagnosis Approach Based on SVM. Iron and Steel, 2007, 42(10): 0-0.
2007, Vol. 42 