蜡样芽孢杆菌DM423生物量的延迟神经网络软测量

Time-delayed neural network-based soft sensor for Baeillus cereus DM423 biomass during batch cultivation

为探索蜡样芽孢杆菌DM423有效的生物量在线测量方法,应用延时神经网络对分批培养过程中DM423的生物量进行软测量,构建了拓扑结构为11–20–1的延时神经网络。网络的输入量为p H、温度、溶氧量和葡萄糖浓度在t–1与t–2时的延时量以及生物量浓度在t–1、t–2和t–3时的延时量,输出量为t时刻的生物量浓度。结果表明,构建网络的泛化能力较好,测试样本的均方差为0.15?10–3,所建立的延时神经网络具有良好鲁棒性和一步预测能力,而多步预测能力不太理想。

Baeillus cereus DM423 is a new replacement of antibody in stock breeding. In this paper, the biomass of Baeillus cereus DM423 during batch cultivation was measured by the soft sensor of time-delayed neural network, which was constructed with the topology of 11-20-1, the input variables was delays of p H, temperature, dissolved oxygen, glucose concentration at t–1 and t–2 and delays of biomass concentration at t–1, t–2 and t–3, and the output variable was biomass concentration at present time. The result showed that the constructed network had good generalization with the mean square error of 0.15 ？10–3 for the testing samples and good robustness and prediction ability.