Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation
Rowe, Raymond C. York, Peter
Rowe, Raymond C.
York, Peter
Publication Date
2009-07-14
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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.
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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.
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