摘要
为利用神经网络的非线性处理能力准确反映微生物培养的动态过程,应用部分反馈神经网络(PRNN)对分批培养过程中的Bacillus cereus DM423的生物量进行软测量,构建了拓扑结构为11-5-1的部分反馈神经网络.网络的输入量为pH、温度、溶氧量和葡萄糖浓度的延时量,同时将网络输出的生物量浓度进行延时、反馈作为网络输入量,输出量为生物量浓度当时值,算法为BPTT法,获得的网络泛化能力较好,训练样本的均方差为0.56×10-3.此外,所建立的部分反馈神经网络具有良好鲁棒性,可抵抗小幅度的高斯噪声干扰.对Bacillus cereus DM423分批培养过程进行多步预测,预测精度高.
Neural networks with nonlinearity correctly describe the dynamic process of microorganism cultivation. In this paper, the biomass of Bacillus cereus DM423 during a batch cultivation was measured by a soft-sensor based on the partial recurrent neural network （PRNN） , and a PRNN with the topology of 11-5-1 was constructed, in which the pH value, the temperature, the dissolved oxygen content, the glucose concentration at two previous times, as well as the delays and feedbacks of estimated biomass concentration at three previous times, were used as the input variables, the current biomass concentration was used as the output variable, and the BPTT algorithm was employed. The results show that the constructed network is of good generalization and that a mean square error of 0. 56× 10^-3 is attained. It is also found that the the constructed network is robust in resisting low Gaussian noise, and is suitable for the accurate multi-step prediction of biomass of Bacillus cereus DM423 during a batch cultivation.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第4期111-115,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金重点资助项目(20436020)
关键词
反馈神经网络
生物量
软测量
预测
recurrent neural network
biomass
soft-sensing
prediction
