基于KRLS的pH中和过程建模  

Modeling for pH neutralization process based on KRLS

作　　者：朱瑞鹤 李军[1] ZHU Rui-he;LI Jun(College of Electrical Engineering and Automation,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《传感器与微系统》2019年第1期48-51,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(51467008)

摘　　要：针对典型的pH酸碱中和过程,提出基于核递推最小二乘(KRLS)的核学习动态模型。KRLS方法采用基于近似线性依赖技术的稀疏化算法,降低了计算复杂度及存储量,能适用于较大规模数据集的训练以及动态时变过程的建模。将所提方法应用到具有缓冲流的双输出中和过程实例中,为验证其有效性,在同等条件下,还与核偏最小二乘(KPLS)、核主成分分析—支持向量机(KPCA-SVM)、核极限学习机(KELM)、极限学习机(ELM)、支持向量机(SVM)等方法进行比较。实验结果表明:作为一种在线自适应方法,KRLS方法具有很高的动态建模精度,为研究pH中和过程的控制奠定了基础。Aiming at typical pH neutralization process,a kernel learning dynamic model based on kernel recursive least squares( KRLS) is proposed. The KRLS method uses sparse algorithm based on approximate linear dependence technology,which reduces computational complexity and storage capacity. It can be applied to the training of large scale data sets and modeling of dynamic time-varying process. The kernel learning method is applied to two-output pH neutralization process with buffered stream. In order to validity the effectiveness of the proposed kernel-based modeling method,under the same conditions,compared with kernel partial least square( KPLS),kernel principal component analysis-support vector machine( KPCA-SVM),extreme learning machine with kernel( KELM),extreme learning machine( ELM),support vector machine( SVM) methods,etc.Experimental results show that as an online adaptive method,KRLS method has high dynamic modeling precision,this study lay the foundation for controlling of pH neutralization process.

关 键 词：PH中和过程 核递推最小二乘 非线性系统 动态建模 稀疏化 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]