文献类型：期刊文献

中文题名：人工神经网络在红霉素发酵过程状态预估中的应用

英文题名：Application of Artificial Neural Network to State Estimation of Process of Erythromycin Fermentation

作者：黄明志[1];杭海峰[1];储炬[1];叶勤[1];张嗣良[1]

机构：[1]华东理工大学生物反应器工程国家重点实验室,上海200237

年份：2000

卷号：26

期号：2

起止页码：162

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;EI(收录号：2000075253520);Scopus;北大核心:【北大核心1996】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：神经网络;BP网络;RBF网络;红霉素;发酵;状态预估

外文关键词：artificial neural network; BP network; RBF network; erythromycin fermentation

摘要：探索了动态 BP网络和 RBF网络在红霉素发酵过程状态预估中的应用 ,比较了它们的收敛速度和学习能力。结果表明 ,BP网络和 RBF网络都具有相当好的学习能力 ,但 RBF网络的收敛速度更快。训练好的神经网络 ,在红霉素发酵过程中可在线预估出红霉素效价、葡萄糖浓度、NH2 - N浓度、丙醇浓度和菌体浓度等参数值 ,并可在进一步的过程优化和控制中应用。
The application of dynamic BP network and RBF network to the state estimation of process of erythromycin fermentation was studied. Their converge speed and learning capability were compared. The research indicated that each of BP network and RBF network had quite good learning capability, but the converge speed of RBF was faster. The potency of erythromycin, glucose concentration, NH 2 N concentration, propanol concentration and cell concentration can be online estimated by these trained artificial neural networks. Therefore, these trained networks can be used in the research of online optimization and control of erythromycin fermentation processes in the future.