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