TSK fuzzy systemsdynamic rule weightsstacked structureoutlier detectionFuzzy rules are very important in Takagi-Sugeno-Kang (TSK) fuzzy systems as they not only provide a mapping mechanism for input patterns but also make fuzzy systems interpretable. Current works further introduce rule weights to ...
TSKThe Sleuth Kit(UNIX) TSKThe Silent Killer(gaming clan) TSKTesked(Swedish: teaspoon) TSKTall Skinny Kiwi TSKThe Subtle Knife(Phillip Pullman book) TSKSkin Temperature TSKTurk Silahli Kuvvetleri(Turkish Armed Forces) TSKTakagi-Sugeno-Kang(fuzzy network model) ...
To overcome this problem, a strategy is presented in this paper to construct a Takagi-Sugeno-Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed ...
Abstract:Ensemble learning is one of the most popular methods for nonlinear systems.However,when the tradi-tional ensemble models of TSK fuzzy classifiers are directly applied to imbalanced data,their learning performances will be deteriorated with poor generalization ability.In order to tackle with this...
In this paper, a subtractive clustering identification algorithm is introduced to model type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLS). The type... QRQ Ren,L Baron,M Balazinski - Fuzzy Information Processing Society, Nafips Meeting of the North American 被引量: 98发表: 2006年 IC...
The parameter conditions are developed for both single-input and multi-input TSK fuzzy systems where the involved fuzzy membership functions are differentiable everywhere or bar some finite points. The derived monotonicity conditions consist of two parts: the conditions on the consequent parts and the ...
The proposed system enhances the conventional TSK inference in two ways: (1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base and (2) simplifying complex fuzzy inference systems by using more compact rule bases for ...
Compare with the traditional quadratic programming of zero-order TSK fuzzy systems, our algorithm does not need to tune the parameters in if-parts of fuzzy rules which are often obtained by clustering technologies. We obtain them randomly. In addition, for the parameter learning of then-parts...
A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi–Sugeno–Kang (TSK) inference. Fuzzy i
A multi-view semi-supervised Takagi鈥揝ugeno鈥揔ang (MV-SS-TSK) fuzzy system is developed for EEG emotion classification in this paper. In the learning of fuzzy system consequent, firstly, a novel joint learning of semi-supervised learning, sparse representation, and low-rank coding is ...