中文摘要：

针对单神经网络模型外推效果不理想、泛化能力较差的缺点，将神经网络集成用于诺西肽发酵过程的建模．采用Bagging技术进行重复取样用于个体神经网络的训练，结论生成时采用加权平均法，各子网络的权重利用差分进化算法来确定,个体神经网络选用典型的动态神经网络Elman网络，通过对多个Elman神经网络模型的输出进行融合，建立了基于神经网络集成的诺西肽发酵产物浓度模型,最后将所建立的模型与基于单神经网络的模型进行了比较，结果说明该模型具有更高的精度和泛化能力.

英文摘要：

In order to improve the poor extrapolation effect and generalizability of the single neural network, the neural network ensemble is used to develop the model of Nosiheptide fermentation process. Each individual network is trained on a bootstrap re-sampling replication of the original training data through the Bagging approach. Then, outputs of the individual neural networks are combined to form an overall output of neural network ensemble through the weighted average method, in which the weight of each individual network is determined by the differential evolution algorithm. The Elman network, a typical dynamic neural network, is applied in each individual network. The model of Nosiheptide fermentation product concentration, based on the neural network ensemble, is thus developed through combination of outputs from multi-Elman neural networks. This model is compared with the single neural network model to illustrate its high accuracy and generalizability.