A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
This work addresses a domain-specific problem in fuzzy model identification for sparse data, representing an incremental improvement over existing methods.
The paper tackles the problem of degraded performance of fuzzy c-regression clustering on sparse data by proposing a novel student-t distribution-based membership function and a Bayesian approach with Gaussian priors to avoid overfitting, resulting in outperforming state-of-the-art methods on standard datasets.
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.