Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
Dynamic data mining technique for rules extraction in a process of battery charging
Applied Soft Computing
Systematic design of a stable type-2 fuzzy logic controller
Applied Soft Computing
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
Fuzzy wavelet neural network for prediction of electricity consumption
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Interval type-2 fuzzy membership function generation methods for pattern recognition
Information Sciences: an International Journal
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
Uncertain Fuzzy Clustering: Insights and Recommendations
IEEE Computational Intelligence Magazine
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comments on “Dynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN)”
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Approximation accuracy of some neuro-fuzzy approaches
IEEE Transactions on Fuzzy Systems
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters
IEEE Transactions on Fuzzy Systems
On stabilization of gradient-based training strategies for computationally intelligent systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
Interval Type-2 Fuzzy Logic Systems Made Simple
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means
IEEE Transactions on Fuzzy Systems
Fuzzy adaptive filters, with application to nonlinear channel equalization
IEEE Transactions on Fuzzy Systems
A fuzzy logic system for channel equalization
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Predictable type-2 fuzzy mobile units for energy balancing in wireless sensor networks
Information Sciences: an International Journal
Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling
Information Sciences: an International Journal
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Expert Systems with Applications: An International Journal
Interval type-2 fuzzy PID load frequency controller using Big Bang-Big Crunch optimization
Applied Soft Computing
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Hybrid-fuzzy modeling and identification
Applied Soft Computing
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The integration of fuzzy systems and neural networks has recently become a popular approach in engineering fields for modelling and control of uncertain systems. This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization of time-varying channels using clustering and gradient algorithms. It combines the advantages of type-2 fuzzy systems and neural networks. The type-2 fuzzy system allows handling the uncertainties associated with information or data in the knowledge base of the process. The structure of the proposed type-2 TSK fuzzy neural system (FNS) is given and its parameter update rule is derived, based on fuzzy clustering and gradient learning algorithm. The proposed structure is used for identification and noise equalization of time-varying systems. The effectiveness of the proposed system is evaluated by comparing the results obtained by the use of models seen in the literature.