Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation.
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.
Shao
Qun
Q
Institute of Pharmaceutical Innovation, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK. q.shao@bradford.ac.uk
Rowe
Raymond C
RC
York
Peter
P
D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations; D012984:Software; D013607:Tablets
D004364:Pharmaceutical Preparations; D013607:Tablets