document: Fuzzy system modeling in pharmacology : an improved algorithm 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 -LRB- i.e. , number of clusters -RRB- , the projection schema -LRB- i.e. , input membership assignment and rule determination -RRB- , 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
doc_bio_tags: B I I O B O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B I O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: fuzzy system modeling pharmacology cluster validity problem projection schema significant input determination predictive performance pharmacokinetic modeling
abstractive_keyphrases: fuzzy sets fuzzy logic