Information Sciences: an International Journal
On decision-making with non-standard fuzzy subsets
International Journal of Knowledge Engineering and Soft Data Paradigms
Reduced-set vector-based interval type-2 fuzzy neural network
WSEAS Transactions on Computers
A Type-1 Approximation of Interval Type-2 FLS
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Information Sciences: an International Journal
Reduced-set vector learning based on hybrid kernels for interval type 2 fuzzy modeling
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Identification and control of time-varying plants using type-2 fuzzy neural system
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Control of the TORA system using SIRMs based type-2 fuzzy logic
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
α-plane representation for type-2 fuzzy sets: theory and applications
IEEE Transactions on Fuzzy Systems
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems
IEEE Transactions on Fuzzy Systems
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Relational type-2 interval fuzzy systems
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Learning methods for type-2 FLS based on FCM
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Journal of Mathematical Imaging and Vision
Overview of Type-2 Fuzzy Logic Systems
International Journal of Fuzzy System Applications
A new indirect approach to the type-2 fuzzy systems modeling and design
Information Sciences: an International Journal
Interval type-2 fuzzy logic based antenatal care system using phonocardiography
Applied Soft Computing
Hi-index | 0.00 |
Fuzzy system modeling (FSM) is one of the most prominent tools that can be used to identify the behavior of highly nonlinear systems with uncertainty. Conventional FSM techniques utilize type 1 fuzzy sets in order to capture the uncertainty in the system. However, since type 1 fuzzy sets express the belongingness of a crisp value x' of a base variable x in a fuzzy set A by a crisp membership value muA(x'), they cannot fully capture the uncertainties due to imprecision in identifying membership functions. Higher types of fuzzy sets can be a remedy to address this issue. Since, the computational complexity of operations on fuzzy sets are increasing with the increasing type of the fuzzy set, the use of type 2 fuzzy sets and linguistic logical connectives drew a considerable amount of attention in the realm of fuzzy system modeling in the last two decades. In this paper, we propose a black-box methodology that can identify robust type 2 Takagi-Sugeno, Mizumoto and Linguistic fuzzy system models with high predictive power. One of the essential problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, discrete interval valued type 2 fuzzy system models are proposed with type reduction. In the proposed fuzzy system modeling methods, fuzzy C-means (FCM) clustering algorithm is used in order to identify the system structure. The proposed discrete interval valued type 2 fuzzy system models are generated by a learning parameter of FCM, known as the level of membership, and its variation over a specific set of values which generate the uncertainty associated with the system structure