Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation

Description

No / 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.

Links & Downloads

1. Shao, Q., Rowe, R. C., York, P. (2006). Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eurpoean Journal of Pharmaceutical Sciences. Vol. 28 No. 5, pp. 394-404.
2. http://hdl.handle.net/10454/2998
3. https://doi.org/10.1016/j.ejps.2006.04.007

Tags

Neurofuzzy logic

Neural Networks

Modelling

Experimental data

immediate tablet release formulation

Additional Fields

Identifer	oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2998
Date	14 July 2009
Creators	Shao, Qun, Rowe, Raymond C., York, Peter
Source Sets	Bradford Scholars
Language	English
Detected Language	English
Type	Article, No full-text in the repository