Fuzzy system modeling in pharmacology: an improved algorithm

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In this paper, we propose an improved fuzzy system modeling algorithm to address some of the limitations of the existing approaches identified during our modeling with pharmacological data. This algorithm differs from the existing ones in its approach to the cluster validity problem (i.e., number of clusters), the projection schema (i.e., input membership assignment and rule determination), and significant input determination. The new algorithm is compared with the Bazoon–Turksen model, which is based on the well-known Sugeno–Yasukawa approach. The comparison was made in terms of predictive performance using two different data sets. The first comparison was with a two variable nonlinear function prediction problem and the second comparison was with a clinical pharmacokinetic modeling problem. It is shown that the proposed algorithm provides more precise predictions. Determining the degree of significance for each input variable, allows the user to distinguish their relative importance.

Fuzzy sets
Fuzzy logic
Fuzzy system modeling
Pharmacokinetic modeling
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