Identification and Convergence Analysis of Interval Type-2 Takagi–Sugeno–Kang Fuzzy Systems for High-Dimensional Classification Problems

In this article, a new defuzzification algorithm is proposed for multiclassification problems, which can effectively improve the accuracy, stability, and computational efficiency of interval type-2 fuzzy systems. In addition, in order to enable the fuzzy system to handle high-dimensional data, this article also designs a collaborative feature selection strategy based on gate function and Group $L_{0}$ regularization, which effectively solves the challenges faced by fuzzy systems when dealing with high-dimensional problems. The strategy allows the system to select relevant features and alleviate the curse of dimensionality. Finally, we employ a root mean square propagation algorithm to simultaneously optimize the antecedent and consequent parameters in the interval type-2 Takagi–Sugeno–Kang fuzzy system, and conduct a convergence analysis of the algorithm to ensure the validity and reliability of the proposed method. To verify the performance of the proposed algorithm, we conducted simulation experiments on high-dimensional datasets. The results demonstrate the superiority of our method in handling multiclassification tasks and the ability to handle complex high-dimensional data.