摘要
针对工业过程中由于时延问题造成软测量模型预测精度不高的问题,给出了一种通过差分进化算法估计时延的动态软测量方法。通过偏最小二乘法建立合适的适应度函数,将软测量系统的时延参数估计问题转化为一个多维非线性优化问题,然后利用差分进化算法的全局搜索能力求解该优化问题。针对PTA精制过程中的PTA平均粒径大小建模研究,结果表明,时延参数估计的引入大大提高了软测量模型的预测精度,证实了所提方法的有效性和可行性。
Aiming at the problem that the prediction accuracy of soft sensor model is not high due to the delay problem in industrial process,a dynamic soft sensor method using differential evolution algorithm to estimate the delay is proposed.The problem of time delay estimation of soft measurement system is transformed into a multi-dimensional nonlinear optimization problem by establishing a suitable fitness function by partial least square method,and then the global search ability of differential evolution algorithm is used to solve the optimization problem.Aiming at the modeling of PTA mean particle size in PTA refining process,the results show that the introduction of delay parameter estimation greatly improves the prediction accuracy of soft sensor model,and the validity and feasibility of the proposed method are verified.
作者
郭储磊
Guo Chulei(Automatic College,Nanjing University of Posts and Telecommunications,Nanjing 210000,China)
出处
《信息技术与网络安全》
2022年第2期46-52,共7页
Information Technology and Network Security
关键词
软测量
差分进化
时延估计
偏最小二乘法
soft sensor
differential evolution
time delay estimation
partial least squares
