信息与控制2011，Vol.40Issue(1)：110-114,5.DOI:10.3724/SP.J.1219.2011.00110
基于免疫遗传算法的动态递归模糊神经网络在发酵过程中的应用
Application of Dynamic Recursive Fuzzy Neural Network Based on Immune Genetic Algorithm to Fermentation Process
摘要
Abstract
In order to solve the problem of the existence of gross errors in data samples for soft sensing modeling, the complexity of the dynamic recursive fuzzy neural network's structure, and the difficulty in determining the massive parameters, a soft sensor based on immune genetic algorithm and dynamic recursive fuzzy neural network is proposed.Similarities between samples are analyzed by the way of computing Mahalanobis distance, the gross errors in data sample are removed to increase the computing speed.In addition, subtractive clustering is applied to determining the number of fuzzy rules in order to simplify the network structure, and at the same time an immune genetic algorithm is introduced to optimize the model parameters to enhance and its precision and generalization ability.The method is applied to biomass concentration soft measurement in the lysine fermentation process.The simulation example shows that the model has high prediction precision and good generalization ability, and it satisfies the need of spot measurement关键词
马氏距离/免疫遗传算法/动态递归模糊神经网络/赖氨酸/软测量分类
信息技术与安全科学引用本文复制引用
孙玉坤,张瑶,黄永红,孙晓天..基于免疫遗传算法的动态递归模糊神经网络在发酵过程中的应用[J].信息与控制,2011,40(1):110-114,5.基金项目
国家863计划资助项目(2007AA04Z179). （2007AA04Z179）
