Multiple kernel least square support vector machine model for prediction of cement clinker lime content

被引：0
作者：
Zhao P. [1 ,2 ]
Liu B. [1 ,2 ]
Gao W. [3 ]
Zhao Z. [1 ,2 ]
Wang M. [1 ,2 ]
机构：
[1] Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei
[2] Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao, 066004, Hebei
[3] Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei
来源：
Huagong Xuebao/CIESC Journal | 2016年 / 67卷 / 06期
关键词：
Algorithm; Least square support vector machine; Model; Multiple kernel learning; Optimization; Random perturbation;
D O I：
10.11949/j.issn.0438-1157.20151598
中图分类号：
学科分类号：
摘要：
Aiming at the problem of how to accurately predict the cement clinker fCaO content, the traditional single kernel least squares support vector machine (LSSVM) is difficult to show the complex non-linear relation between the clinker fCaO content and corresponding variables exactly. Thus, the multiple kernel least square support vector machine (MKLSSVM) containing three kernel function is presented based on multiple kernel learning to avoid the influence of the single kernel function on the model accuracy. As a result of artificial selection the parameters of MKLSSVM is blindness and uncertainty. The random perturbation chaos particle swarm optimization (RPCPSO) algorithm is presented to get the best parameters of MKLSSVM. The cement clinker fCaO content model is built by using the RPCPSO algorithm to optimize the parameters of MKLSSVM. Simulation results indicated that the RPCPSO algorithm had a fast convergence speed, and the model had high precision and strong ability of power generalization. Thus, the model was valuable for practical application. © All Right Reserved.
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页码：2480 / 2487
页数：7
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