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    Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithm.

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    Publication date
    2005
    Author
    Rowe, Raymond C.
    Plumb, A.P.
    York, Peter
    Brown, M.
    Keyword
    Artificial neural network (ANN);
    Network architecture
    Training algorithm
    Direct compression tablet formulation
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    The purpose of this study was to determine whether artificial neural network (ANN) programs implementing different backpropagation algorithms and default settings are capable of generating equivalent highly predictive models. Three ANN packages were used: INForm, CAD/Chem and MATLAB. Twenty variants of gradient descent, conjugate gradient, quasi-Newton and Bayesian regularisation algorithms were used to train networks containing a single hidden layer of 3¿12 nodes. All INForm and CAD/Chem models trained satisfactorily for tensile strength, disintegration time and percentage dissolution at 15, 30, 45 and 60 min. Similarly, acceptable training was obtained for MATLAB models using Bayesian regularisation. Training of MATLAB models with other algorithms was erratic. This effect was attributed to a tendency for the MATLAB implementation of the algorithms to attenuate training in local minima of the error surface. Predictive models for tablet capping and friability could not be generated. The most predictive models from each ANN package varied with respect to the optimum network architecture and training algorithm. No significant differences were found in the predictive ability of these models. It is concluded that comparable models are obtainable from different ANN programs provided that both the network architecture and training algorithm are optimised. A broad strategy for optimisation of the predictive ability of an ANN model is proposed.
    URI
    http://hdl.handle.net/10454/3011
    Version
    No full-text available in the repository
    Citation
    Rowe, R.C., Plumb, A.P., York, P. and Brown, M. (2005) Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithm. European Journal of Pharmaceutical Sciences. Vol. 25, No. 4-5, pp. 395-405.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.ejps.2005.04.010
    Type
    Article
    Collections
    Life Sciences Publications

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