目的 为防治中药水提液陶瓷膜纯化过程中的膜污染问题,探索预测污染度的新方法.方法 对BP神经网络模型进行改进,利用隐含层最佳神经元数目的快速确定方法、BP神经网络权值和阈值的快速寻优算法,建立中药水提液陶瓷膜污染预测模型,对207组中药水提液数据进行网络训练和预测.结果 与多元回归分析、基本BP神经网络、RBF神经网络等模型相比,应用改进BP神经网络模型进行预测实验的拟合误差更小,均方误差仅为0.0057;此外,改进BP神经网络模型的性能更加稳定,在20次随机运行实验中达到预设目标的成功率高达95%.结论 改进模型具有很好的网络性能、拟合效果和预测能力,能够稳定准确地预测膜污染度.%Objective To prevent and treat of ceramic membrane purification of membrane fouling process of TCM extracts; To explore new methods of forecasting membrane fouling degree.Methods BP neural network model was improved. Methods to fast determine the optimal number of neurons in the hidden layer and fast algorithm for optimizing the weight and threshold of BP neural network were studied. Data of 207 groups of TCM extracts were under network training and prediction.Results Compared with the models of multiple regression analysis, basic BP neural network and RBF neural network, the error of the improved BP neural network model was less than that of the BP neural network model, and the mean square error was only 0.0057. In addition, the improved BP neural network model performance was more stable. In the 20 random running experiments, the goal of the success rate achieved up to 95%.Conclusion The improved model has a good network performance, the fitting effect and prediction ability, and can forecast the fouling degree of membrane stably and accurately.
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