造粒
人工神经网络
计算机科学
易碎性
人工智能
机器学习
数学
工程类
材料科学
岩土工程
乙基纤维素
复合材料
聚合物
作者
Jothi G. Kesavan,Garnet E. Peck
标识
DOI:10.3109/10837459609031434
摘要
Current-day pharmaceutical formulation may be trial and error in nature due to the absence of a clear relationship between the formulation characteristics (output variables) and the material and process variables (input variables). Neural networks are networks of adaptable nodes, which through a process of learning from task examples, store experiential knowledge and make it available for prediction. Prediction of a model granulation and tablet system characteristics from the knowledge of material and process variables utilizing neural networks is the basis of this presentation. The formulation design contained the following variables: granulation equipment, diluent, method of binder addition, and the binder concentration. The material, process, granulation evaluation, and tablet evaluation data of the formulations were used as the data set for training and testing of the neural network models. A comparison of the neural network prediction performance with that of regression models was also done. Both the granulation model and the tablet model converged fairly rapidly in the training step. In the testing step, the predictions for all granulation model variables (geometric mean particle size, flow value, bulk density, and tap density) were satisfactory. In the tablet model, the predictions for disintegration and thickness were also satisfactory. The predictions for hardness and friability were less than satisfactory. Two situations where the neural network may not perform adequately are discussed. The neural network prediction is better or comparable for all the predicted variables in this study compared to regression methods. The results clearly show the applicability of neural networks to formulation modeling.
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