基于RBF神经网络的诺西肽发酵过程菌体质量浓度软测量  

Neural Network Based Soft Sensor of Biomass in Nosiheptide Fermentation Process

作　　者：杨强大[1] 张卫军[1] 王福利[2] 常玉清[2] Qiang-da Wei-jun Fu-li Yu-qing 

机构地区：[1]东北大学材料与冶金学院,辽宁,沈阳,110004 [2]东北大学流程工业综合自动化教育部重点实验室,辽宁,沈阳,110004

出　　处：《计量学报》2009年第6期-,共5页Acta Metrologica Sinica

基　　金：国家自然科学基金，国家重点基础研究发展规划(973计划)

摘　　要：针对诺西肽发酵过程中菌体质量浓度的估计问题,提出了一种基于RBF神经网络的软测量建模方法.在诺西肽发酵过程非结构模型的基础上,根据隐函数存在定理确定出辅助变量,从而使其选择有严格的理论依据;根据每批样本数据对被预测对象的预估能力,自适应地为各个批次的训练样本分配权值,并进而实施加权RBF神经网络建模.实际应用表明,所提出的软测量建模方法是有效的. Abstract： A RBF neural network based soft sensor method is presented for the estimation of biomass in Nnsiheptide fermentation process. Based on the unstructured model of Nosiheptide fermentation process, the secondary variables are selected according to the implicit function existence theorem, which makes the selection be strict in theory. Each batch training samples are self-adaptively weighted according to their different predicting ability to the predicted object, and then weighted RBF neural network (WRBFNN) is applied to develop the biomass soft sensor modeL The testing result shows that the presented method is effective.

关 键 词：RBF神经网络 诺西肽发酵过程 菌体质量浓度 软测量 FERMENTATION Process SOFT SENSOR Based NETWORK FERMENTATION process RBF neural NETWORK SOFT SENSOR implicit function existence theorem 建模方法 隐函数存在定理 SENSOR modeL model different 预测对象 训练样本 

分 类 号：TB9-55[一般工业技术—计量学]