Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2482709 | European Journal of Pharmaceutical Sciences | 2006 | 11 Pages |
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.
Related Topics
Health Sciences
Pharmacology, Toxicology and Pharmaceutical Science
Drug Discovery
Authors
Qun Shao, Raymond C. Rowe, Peter York,