人工神经网络在红霉素发酵过程状态预估中的应用

探索了动态ＢＰ网络和ＲＢＦ网络在红霉素发酵过程状态预估中的应用，比较了它们的收敛速度和学习能力。结果表明，ＢＰ网络和ＲＢＦ网络都具有相当好的学习能力，但ＲＢＦ网络的收敛速度更快，训练好的神经网络，在红霉素发酵过程中可在线预估出红霉素效价、葡萄糖浓度、ＮＨ２－Ｎ浓度、丙醇浓度和菌体浓度等参数值，并可在进上步的过程优化和控制中应用。

Application of Artificial Neural Network to State Estimation of Process of Erythromycin Fermentation

The application of dynamic BP network and RBF network to the state estimation of process of erythromycin fermentation was studied. Their converge speed and learning capability were compared. The research indicated that each of BP network and RBF network had quite good learning capability, but the converge speed of RBF was faster. The potency of erythromycin, glucose concentration, NH 2 N concentration, propanol concentration and cell concentration can be online estimated by these trained artificial neural networks. Therefore, these trained networks can be used in the research of online optimization and control of erythromycin fermentation processes in the future.