Shao Qun, Rowe Raymond C, York Peter
Institute of Pharmaceutical Innovation, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK.
Eur J Pharm Sci. 2006 Aug;28(5):394-404. doi: 10.1016/j.ejps.2006.04.007. Epub 2006 Apr 29.
This study compares the performance of neurofuzzy logic and neural networks using two software packages (INForm and FormRules) in generating predictive models for a published database for an immediate release tablet formulation. Both approaches were successful in developing good predictive models for tablet tensile strength and drug dissolution profiles. While neural networks demonstrated a slightly superior capability in predicting unseen data, neurofuzzy logic had the added advantage of generating rule sets representing the cause-effect relationships contained in the experimental data.
本研究使用两个软件包（INForm和FormRules）比较了神经模糊逻辑和神经网络在为已发表的速释片剂配方数据库生成预测模型方面的性能。两种方法都成功地为片剂抗张强度和药物溶出曲线开发了良好的预测模型。虽然神经网络在预测未见数据方面表现出略高的能力，但神经模糊逻辑具有生成代表实验数据中因果关系的规则集的额外优势。
