摘要
针对海洋生物酶发酵过程中关键生物参数在线测量困难、离线化验滞后大,难以实现实时控制问题,提出一种贝叶斯正则化神经网络软测量建模方法。通过贝叶斯正则化修正神经网络训练性能函数,限制网络权值,简化了网络结构,从而有效地抑制过拟合,提高网络泛化能力。以典型的海洋生物酶—海洋蛋白酶为例,建立了海洋蛋白酶发酵过程关键生物参数(基质浓度、菌体浓度、酶活)软测量模型。结果表明:贝叶斯正则化神经网络不仅对训练样本集表现出很强的拟合能力,且对预测样本同样表现出很强的推广能力;在预测精度上明显高于LM算法的神经网络,解决了海洋生物酶发酵过程关键生物参数在线测量问题。
Aiming at the problem that crucial biological parameters are difficult to be measured online,and offline measuring cannot satisfy the needs of real-time optimized control due to the problem of big time-lag during the marine biological enzyme fermentation process,a soft measurement modeling method based on Bayesian regularization neural network is proposed. In this method,Bayesian regularization is used to limit the weights of network by modifying the neural network training property function,so the network structure is simplified,over fitting is restrained effectively and the generalization capability of neural network is enhanced. This method is applied to marine protease enzyme（ typical marine biological enzyme） fermentation process,measurement model for crucial biological parameters（ such as substrate concentration,biomass concentration and enzyme activity） for marine protease enzyme fermentation process is established. Simulation results show that Bayesian regularization neural network not only has strong capability of fitting for the training sample sets,and also has a strong generalization ability for prediction samples,the prediction precision is significantly higher than the LM algorithm neural network. The problem of on-line measurement of crucial biological parameters is solved for marine biological enzyme fermentation process.
作者
孙丽娜
黄永红
丁慎平
刘骏
SUN Li-na1, HUANG Yong-hong2 , DING Shen-ping1 , LIU Jun1(1. Department of Mechatronics Engineering, Suzhou Industrial Park Institute of Vocational Technology, Suzhou 215123, China; 2. College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)
出处
《传感器与微系统》
CSCD
2018年第9期136-138,共3页
Transducer and Microsystem Technologies
基金
江苏高校优势学科建设工程资助项目(PAPD)
"十二五"国家"863"计划重点科技资助项目(2011AA09070301)
江苏省自然科学基金面上资助项目(BK20151345)
江苏高校品牌专业建设工程资助项目(PPZY2015A088)
关键词
贝叶斯正则化
神经网络
关键生物参数
软测量
Bayesian regularization
neural network
crucial biological parameters
soft measurement
