基于RVM组合核优化的软测量模型研究

doi:10.16182/j.issn1004731x.joss.201801035

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (1): 272-277.doi: 10.16182/j.issn1004731x.joss.201801035

基于RVM组合核优化的软测量模型研究

张亚男, 杨慧中

江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122

收稿日期:2015-10-22 发布日期:2019-01-02
作者简介:张亚男(1993-),女,江苏盐城,硕士,研究方向为工业建模及数据挖掘;杨慧中(1955-),女,江苏无锡,博士,教授,博导,研究方向为过程建模及水质监测等。
基金资助:
国家自然科学基金(61273070)

RVM Soft Sensing Model Based on Optimized Combined Kernel Function

Zhang Yanan, Yang Huizhong

Key Laboratory of Advanced Control of Light Industry Process in the Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2015-10-22 Published:2019-01-02

摘要/Abstract

摘要： 提出了一种基于RVM(Relevance Vector Machine,RVM)组合核的软测量建模方法。为同时得到较强的回归能力和较好的稀疏性,对RVM构造一个组合核函数的同时,又构建了一个综合回归性能和稀疏性的适应度函数,利用遗传算法优化RVM组合核的权系数和核参数。将该方法用于一个双酚A生产流程中裂解回收单元的建模仿真,实例表明,所提方法的预测精度、稀疏性等指标均优于一般的SVM组合核模型和GA-RVM单一核模型。

Abstract: An RVM spft sensingmodeling method based onthe optimizedcombined kernel functionis proposed.In order to simultaneously get better prediction and sparsity, a fitness function synthesizing regression accuracy and sparsity is created while constructing a combined kernel functionfor RVM.The genetic algorithm is used to optimize the weights and kernel parametersof the RVMcombined kernel.The proposed method is used totomodela cleavage-recovery unit in the production process of Bisphenol-A.The results show that it can guarantee better sparsity andregression accuracy than the general SVM combinedkernel model andGA-RVM single kernel model.

Key words: bisphenol A, relevance vector machine, genetic algorithms, fitness function, kernel parameters, mixed kernel function

中图分类号:

TP274

张亚男, 杨慧中. 基于RVM组合核优化的软测量模型研究[J]. 系统仿真学报, 2018, 30(1): 272-277.

Zhang Yanan, Yang Huizhong. RVM Soft Sensing Model Based on Optimized Combined Kernel Function[J]. Journal of System Simulation, 2018, 30(1): 272-277.

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基于RVM组合核优化的软测量模型研究

RVM Soft Sensing Model Based on Optimized Combined Kernel Function

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