On-line Adaptation Algorithm for RBF Kernel Based FS-SVM

摘要：

The performance of Fixed-Size least squares support vector machines (FS-SVM) has been illustrates on the large-scale modeling problem. This paper presents an adaptive RBF kernel based FS-SVM and an on-line adaptation algorithm for time-varying nonlinear systems. The key feature of this algorithm method is the direct approach used for formulating the training target. Based on the feature of RBF kernel, the error (objective) function between actual active model and target model is formulated and can be minimized by Gradient descent algorithm. The proposed algorithm is capable of maintaining the accuracy of learned patterns even when a large number of aged patterns are replaced by new ones through the adaptation process. The simulation results show the effectiveness of this architecture for adaptive modeling.

关键词： Fixed-size LS-SVM RBF kernel On-line Adaptation algorithm Target model

作者: Yuan Ping Mao Zhizhong Wang Fuli

作者单位: with the Automatic Institute of Northeastern University RPC corresponding author to provide was with the Automatic Institute of Northeastern University RPC with the Automatic Institute of Northeastern University RPC

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3971-3975

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）