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

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    Publication date
    2009-07-14T08:09:45Z
    Author
    Shao, Qun
    Rowe, Raymond C.
    York, Peter
    Keyword
    Neurofuzzy logic
    Neural Networks
    Modelling
    Experimental data
    immediate tablet release formulation
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    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.
    URI
    http://hdl.handle.net/10454/2998
    Version
    No full-text available in the repository
    Citation
    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.
    Link to publisher’s version
    10.1016/j.ejps.2006.04.007
    Type
    Article
    Collections
    Life Sciences Publications

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