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.
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 |
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