Common and special knowledge-driven TSK fuzzy system and its modeling and application for epileptic EEG signals recognition

Abstract

Takagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balances between approximation accuracy and interpretability. Among a wide variety of existing TSK fuzzy systems, most of them are driven by special knowledge since the learned parameters of each fuzzy rule are totally different. However, common knowledge is equally important and useful in practice and hence a TSK fuzzy system embedded with common knowledge should be more intuitive and interpretable when tackling with real-world problems. In this paper, we propose a common and special knowledge-driven TSK fuzzy system (CSK-TSK-FS), in which the parameters corresponding to each feature in then-parts of fuzzy rules always keep invariant and these parameters are viewed as common knowledge. As for its modeling, except the gradient descent techniques and other existing training algorithms, we can obtain a trained CSK-TSK-FS from a trained GMM or a trained FLNN because the proposed fuzzy system CSK-TSK-FS is mathematically equivalent to a special GMM and a FLNN. CSK-TSK-FS has three characteristics: (1) with the classical centroid defuzzification strategy, the involved common knowledge can be separated from fuzzy rules such that the interpretability of CSK-TSK-FS can be enhanced; (2) it can be trained quickly by the proposed LLM-based training algorithm; (3) the equivalence relationships among CSK-TSK-FS, GMM and FLNN allow them to share some commonality in training such that the proposed LLM-based training algorithm provides a novel fast training tool for training GMM and FLNN. Experimental results on UCI, KEEL and epileptic EEG datasets demonstrate the promising classification of CSK-TSK-FS.