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A neural-network-based intelligent system for time-series prediction problems in product development

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4 Author(s)
Wei Yee Goh ; Sch. of Ind. Technol., Univ. Sains Malaysia, Penang, Malaysia ; Lim, C.P. ; Kok Khiang Peh ; Subari, K.

We investigate the application of artificial neural networks (ANNs) as an intelligent agent to time-series prediction problems in pharmaceutical product development. The objective of the study is to use the Elman recurrent network as a development tool for predicting in-vitro dissolution profiles of matrix controlled release theophylline pellets-one of the widely used drugs in the treatment of asthma patients. Instead of estimating parameters of certain mathematical models that fit the profiles, a different approach has been applied by using ANN as a model for predicting the whole profile directly. Performance of the network was assessed by evaluating the similarity factor, f2, in accordance with the recommendation of the United State Food and Drug Administration. In addition, reliability of the f2 values was justified by calculating confidence bounds using the bootstrap method. The promising results obtained reveal the potential of the Elman network as a decision support tool to overcome problems in pharmaceutical product formulation

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TENCON 2000. Proceedings  (Volume:1 )

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