A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification

TL;DR

This paper introduces a Bayesian fuzzy c-regression model utilizing a Type-2 Student-t membership function, specifically designed for sparse data, and demonstrates its superior performance over existing methods.

Contribution

It presents a novel Bayesian fuzzy c-regression architecture with a Student-t membership function for sparse data modeling and employs advanced optimization and type-reduction techniques.

Findings

Outperforms state-of-the-art methods on standard datasets.

Effectively models sparse data with improved accuracy.

Reduces overfitting through Bayesian regularization.

Abstract

Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse databut their performance degraded on sparse data. In this paper, aninnovative architecture for fuzzyc-regression model is presentedand a novel student-tdistribution based membership functionis designed for sparse data modelling. To avoid the overfitting,we have adopted a Bayesian approach for incorporating aGaussian prior on the regression coefficients. Additional noveltyof our approach lies in type-reduction where the final output iscomputed using Karnik Mendel algorithm and the consequentparameters of the model are optimized using Stochastic GradientDescent method. As detailed experimentation, the result showsthat proposed approach outperforms on standard datasets incomparison of various state-of-the-art methods.