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The prediction of uncertain and predictive nonlinear systems is an importantand challenging problem. Fuzzy logic models are often a good choice to describesuch systems however in many cases these become complex soon. commonlly, tooless effort is put into descriptor selection and in the creation of suitablelocal rules. Moreover, in common no model reduction is applied, while this mayanalyze the model by removing redundant data. This paper suggests a combinedmethod that deal with these issues in order to create compact Takagi Sugeno(TS) models that can be effectively used to represent complex predictivesystems. A new fuzzy clustering method is come up with for the identificationof compact TS-fuzzy models. The best relevant consequent variables of the TSmodel are choosen by an orthogonal least squares technique based on theobtained clusters.For the selection of the relevant antecedent (scheduling)variables a new method has been developed based on Fisher's interclassseparability basis. This complete approach is demonstrated by means of theOxazolines and Oxazoles derivatives as antituberculosis agent for nonlinearregression benchmark. The results are compared with results obtained byneuro-fuzzy i.e. ANFIS algorithm and advanced fuzzyy clustering techniques i.eFMID toolbox .

Non linear Prediction of Antitubercular Activity Of Oxazolines and  Oxazoles derivatives Making Use of Compact TS-Fuzzy models Through Clustering  with orthogonal least sqaure technique and Fuzzy identification system