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

作　　者：孙玉坤[1] 张瑶[1] 黄永红[1] 孙晓天[2] 

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013 [2]北京化工大学信息科学与技术学院,北京100029

出　　处：《信息与控制》2011年第1期110-114,共5页Information and Control

基　　金：国家863计划资助项目(2007AA04Z179)

摘　　要：针对软测量建模数据中过失误差及动态递归模糊神经网络的结构复杂,大量参数难以确定的情况,提出基于免疫遗传算法的动态递归模糊神经网络软测量方法.利用样本间马氏距离进行样本相似程度分析,去除样本中过失数据以提高计算速度.此外应用减法聚类确定模糊规则数,以简化网络结构,同时应用免疫遗传算法优化模型参数以提高模型的精度和泛化能力.将该方法应用于赖氨酸发酵过程菌体浓度的软测量,仿真结果表明,该方法具有较高的预测精度,满足现场测量要求.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 号：TP273[自动化与计算机技术—检测技术与自动化装置]