A course in fuzzy systems and control
A course in fuzzy systems and control
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
Systematic design of a stable type-2 fuzzy logic controller
Applied Soft Computing
Fuzzy wavelet neural network for prediction of electricity consumption
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Position Paper: Toward extended fuzzy logic---A first step
Fuzzy Sets and Systems
On the stability of interval type-2 TSK fuzzy logic control systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
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
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems
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
Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters
IEEE Transactions on Fuzzy Systems
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning
IEEE Transactions on Fuzzy Systems
A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles
Information Sciences: an International Journal
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
A context layered locally recurrent neural network for dynamic system identification
Engineering Applications of Artificial Intelligence
Overview of Type-2 Fuzzy Logic Systems
International Journal of Fuzzy System Applications
Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling
Information Sciences: an International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Hi-index | 0.00 |
Abstract: A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the gradient descent algorithm used afterwards converges in a shorter time. A number of different load conditions including nonlinear and time-varying ones are used to investigate the performance of the proposed control algorithm. The control structure has the ability to regulate the servo system with reduced oscillations when compared with the results of its type-1 counterpart around the set point signal in the presence of load disturbances.