A New Interval Type-2 Fuzzy Clustering Algorithm for Interval Type-2 Fuzzy Modelling with Application to Heat Treatment of Steel

摘要

In this paper, a new hierarchical data-driven modelling strategy based on Interval Type-2 Fuzzy Clustering is elicited for the Interval Type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System. This framework which we have called the IT2-Squared framework uses interval type-2 fuzzy clustering for initial antecedent parameters and structures determination and least-squares algorithm for deriving initial consequent parameters. To improve the accuracy of the system, we show how the steepest descent algorithm is used to tune the parameters of both the consequent and antecedent parameters. To test the efficacy of this proposed system, the model is used on a real-life engineering project for the prediction of the ultimate tensile strength (UTS) of steel. Results show excellent generalization properties of the IT2-Squared modelling framework when compared to previously elicited models of the same system.