摘要
基于过程先验知识和Kriging模型,针对Kriging模型在求解参数过程中容易陷入局部最优的缺陷,提出一种利用进化优化算法求解模型参数的方法。在参数求解过程中,将过程变量对应的输出函数的单调性和凹凸性先验知识作为约束条件,在合理的样本数目下,得到更符合先验知识的Kriging模型,提高模型的预测精度。通过函数仿真实验和复合纳滤膜的溶胶粒径的预测估计验证了此建模方法的有效性。
Since traditional optimizing methods of Kriging model are easy to result in local optimum,a new method was proposed considering both evolutionary algorithm and priori knowledge.Monotonicity and concave-convexity priori knowledge were used as the constraints in the optimization process.With a reasonable number of samples,the Kriging model becomes more consistent with priori knowledge,which can improve prediction accuracy.Finally,a function simulation experiment and a forecasting model of the average particle size of ZrO2-TiO2colloidal sols were used to demonstrate the effectiveness of the proposed method.
作者
许文夕
李绍军
XU Wen-xi;LI Shao-jun(Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education,East China University of Science and Technology, Shanghai 200237, China)
出处
《高校化学工程学报》
EI
CAS
CSCD
北大核心
2017年第2期442-448,共7页
Journal of Chemical Engineering of Chinese Universities
基金
国家自然科学基金(21406064
21676086)
关键词
KRIGING模型
先验知识
约束处理
智能算法
参数估计
Kriging model
priori knowledge
constraint handling
intelligent algorithms
parameter estimation