基于Hammerstein-Wiener模型的CSTR反应器辨识  

Identification of CSTR based on Hammerstein-Wiener model

作　　者：韩珍珍 成彬[1,2] 王程 王云丽[1,2] Han Zhenzhen;Cheng Bin;Wang Cheng;Wang Yunli(Institute of Applied Mathematics,Hebei Academy of Sciences,Shijiazhuang 050081,China;Information Security Authentication Technology Innovation Center of Hebei Province,Shijiazhuang 050081,China)

机构地区：[1]河北省科学院应用数学研究所,河北石家庄050081 [2]河北省信息安全认证技术创新中心,河北石家庄050081

出　　处：《电子技术应用》2023年第7期30-34,共5页Application of Electronic Technique

基　　金：河北省自然科学基金项目(F2022302001);河北省科学院重点学科提升工程(23A02)。

摘　　要：针对化工过程中广泛应用的连续搅拌反应釜(CSTR)反应器,提出一种新的基于极限学习机的Hammerstein-Wiener模型的辨识建模方法。其中,Hammerstein-Wiener模型的两个非线性环节采用两个不同的极限学习机逼近,线性环节采用自回归ARX模型。因极限学习机的特殊结构,此模型可以表示成线性回归的形式,最终利用广义最小二乘法求解模型的参数。此方法辨识过程简单,辨识过程的计算量较小。最后对CSTR的辨识结果表明,在相同条件下与基于多项式的Hammerstein模型和ARX-LSSVM Hammerstein模型相比,该方法具有较高辨识精度,表明了该方法的有效性。In this paper,an Hammerstein-Wiener model based on extreme learning machine is built to identify Continuous Stirred Tank Reactor(CSTR)nonlinear system which is used in chemical process widely.In the proposed Hammerstein-Wiener model,the two nonlinear blocks are described by two different extreme learning machine neural networks.The linear block is described by ARX model.Due to the special structure of the extreme learning machine,this model can be expressed in the form of linear regression.The model parameter identification is achieved by generalized least square algorithm.The identification process is simple with less computation complexity.The simulation result shows that this proposed approach is effective.Compared with Hammerstein model based polynomial and ARX-LSSVM Hammerstein model,the proposed method has higher identification accuracy.

关 键 词：辨识 HAMMERSTEIN-WIENER模型 极限学习机 CSTR 最小二乘法 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]