GSETSK: a generic self-evolving TSK fuzzy neural network with a novel Hebbian-based rule reduction approach
This paper attempts to achieve a compact, recent, and interpretable fuzzy rule bases. It proposes a novel rule pruning method that is simple and computationally efficient. It proposes a merging approach to improve the interpretability of the knowledge base. GSETSK adopts an online data-driven incremental-learning-based approach. GSETSK derives an up-to-date and interpretable rule base with high level of accuracy.
self-evolving fuzzy neural network neuro-fuzzy systems