MGE-DNTSK: A Multi-Granularity Ellipsoidal Dual-Neural TSK Fuzzy Classification System
Year: 2026
Type: article
Abstract: Traditional Takagi–Sugeno–Kang (TSK) fuzzy systems suffer from three fundamental limitations: Exponential rule growth from Cartesian product-based generation, cross-dimensional interaction constraints imposed by T-norm aggregation, and limited expressiveness of linear consequents. To simultaneously address these shortcomings, this article proposes Multi-Granularity Ellipsoidal Dual-Neural TSK (MGE-DNTSK), a novel neural fuzzy classification system comprising a hierarchical granulation layer and a dual-neural TSK (DNTSK) transformation layer. Unlike existing approaches that tackle these limitations in isolation, MGE-DNTSK unifies differentiable information granulation with neural rule generation within a single end-to-end framework. Specifically, the granulation layer constructs tilted hyper-ellipsoidal granules via low-rank Cholesky factorization across coarse, medium, and fine tiers, encoding inter-feature correlations into membership representations with linear parameter complexity. Building upon these representations, the DNTSK layer replaces Cartesian product-based rule generation and linear consequent functions with a shared neural antecedent incorporating adaptive pruning and independent neural consequents, thereby decoupling rule complexity from input dimensionality while enhancing local nonlinear expressiveness. The entire architecture is optimized through a three-stage curriculum protocol. Experiments on 16 benchmark datasets show that MGE-DNTSK achieves leading performance across four evaluation metrics against 15 representative baselines, particularly on high-dimensional data, and a real-world industrial fault-diagnosis case study further exhibits its practical effectiveness.
Institutions: Dalian University of Technology, University of Alberta
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7202224429