Material properties and tensile strength prediction model of traditional Chinese medicine tablets based on PCA-RBF neural network

• Published in: Zhongguo zhongyao zazhi Vol. 44; no. 24; p. 5390
• Main Authors: Zhao, Hai-Ning, Wang, Ya-Jing, Shang, Li-Na, Zhou, Meng-Nan, Zhang, Yi, Ye, Xiang-Yin, Wang, Yan-Wen, Gao, Di
• Format: Journal Article
• Language: Chinese
• Published: China 01.12.2019
• Subjects: Medicine, Chinese Traditional; Neural Networks, Computer; Powders; Tablets; Technology, Pharmaceutical; Tensile Strength; tensile strength; prediction model; radial basis neural network; powder properties; tablet formability; principal component analysis; traditional Chinese medicine extract
• ISSN: 1001-5302
• DOI: 10.19540/j.cnki.cjcmm.20190916.303

This paper constructs a prediction model of material attribute-tensile strength based on principal component analysis-radial basis neural network( PCA-RBF),in order to predict the formability of traditional Chinese medicine tablets. Firstly,design Expert8. 0 software was used to design the dosage of different types of extracts,the mixture of traditional Chinese medicine with different physical properties was obtained,the powder properties of each extract and the tensile strength of tablets were determined,the correlation of the original input layer data was eliminated by PCA,the new variables unrelated to each other were trained as the input data of RBF neural network,and the tensile strength of the tablets was predicted. The experimental results showed that the PCA-RBF model had a good predictive effect on the tensile strength of the tablet,the minimum relative error was 0. 25%,the maximum relative error was2. 21%,and the average error was 1. 35%,which had a high fitting degree and better network prediction