Time Series Prediction Using Recurrent Neural Networks And Boosting: An Experimental Study In Pharmaceutical Product Formulation
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Date
2002-05
Authors
Wei Yee, Goh
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Abstract
This thesis is devoted to the development of Artificial Neural Network (ANN)
techniques for solving time-series prediction problems. The research is focused on the
use of recurrent neural networks for devising a comprehensible framework for
pharmaceutical product formulation using time series prediction approach. In
particular, the framework explores the learning paradigms of ANNs for conducting the
experimental design and analysis. Based upon existing methodologies, novel ANN
architectures are proposed for time series analyses in the process of pharmac~utical
product formulation.
The Elman recurrent neural network is employed for the prediction of in-vitro
dissolution profiles of matrix-controlled-release theophylline pellets preparations.
Feedback links in this network perform recursive computation, and provide the ability
of dynamical representation of information in time series analyses, especially for nonlinear
dynamical systems. The experimental results have successfully demonstrateĀ¢ the
potentials of the Elman-based networks for formulating pharmaceutical products to
meet the desired drug release characteristics. Furthermore, reliability of the network
performance is statistically assessed using the bootstrap method.
The theoretical foundations and operational strategies of boosting have been elaborated
in details. The standard AdaBoost algorithm that is often employed in classification
tasks is modified to accommodate multiple Elman networks for time-series prediction
problems. Several simulation studies are conducted using benchmark data sets, and the
results are compared with those obtained from other published methods. The results
indicate that multiple Elman networks coupled with the modified AdaBoost algorithm
are capable of improving generalisation of individual networks. In addition, the
pharmaceutical formulation problems are re-visited to assess the practical applicability
of multiple predictors devi~ed based on the Elman networks. The results are evaluated
statistically using hypotheses tests to justify the effectiveness of the proposed system.
In summary, this research work has revealed the benefits of using multiple Elman-based
networks with boosting as a unified and convenient framework for utilizing techniques
in time series prediction for pharmaceutical product formulation.
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Keywords
Computer , Networks And Boosting