Abstract
The design of optimal model based experiments is finding increasing use in various fields including chemical, pharmaceutical, biological engineering, and biomedicine. The traditional Model Based Optimal Experimental Design (MBOED) techniques focus on improving the parameter precision but do not consider the undesired possibility of increasing the correlation among the estimated parameters. In this paper, we propose a multi-objective optimization based experimental design technique, which provides the trade-off curve between information measure and correlation measure in the form of a Pareto-optimal front. This Pareto-optimal front gives the experimenter the freedom to choose appropriate experimental designs for real world system under investigation. The proposed methodology is illustrated using an example involving the identification of physiological models for characterizing Type 1 diabetic patients.
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