基于核参数分时段调节型LSSVM的在线过程辨识方法

Online process identifying based on time-division regulated kernel parameter LSSVM

利用最小二乘支持向量机(least square support vector machine,LSSVM)在线辨识时变非线性过程时,设定其核参数较困难,设定的核参数不能适应过程变化而进行自动调节。针对此问题,提出了一种基于核参数分时段调节型LSSVM的在线过程辨识方法。该方法利用了三个LSSVM,并将整个建模预测时期分为启动阶段和若干个工作周期。初始阶段末和每个工作周期末选定预测误差和最小的LSSVM,作为后续工作周期的工作LSSVM,同时根据启发式规则为另两个LSSVM设定核参数,它们作为后续工作周期的比较LSSVM。该方法设定核参数相对容易,而且核参数具有一定的自动调节能力。数字仿真显示,从统计角度而言,所提方法比传统方法有更好的适应性。

It is difficult setting the kernel parameter which is applied to online identify a time-varing nonlinear process by using least square support vector machine（LSSVM）,and such setted one can not automatically adjust to adapt it to the varying process.In view of this situation,an online process identification approach based on time-division regulated kernel parameter LSSVM is proposed.Three LSSVMs are utilized and the whole modeling predicting times are divided into a starting stage and several working periods.The LSSVM with a smallest sum of predicting errors is selected as a working LSSVM for successive working periods at the end of both the starting stage and each working period.Moreover,kernel parameters are reset for other two LSSVMs according to heuristic rules,and they are used as comparative LSSVMs for the following working periods.The method is easy to set kernel parameters and has adjustability to a certain extent.The numerical simulation shows the adaptability of the method is better than that of traditional methods statistically.