Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Design of interval type-2 fuzzy sliding-mode controller
Information Sciences: an International Journal
Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems
Engineering Applications of Artificial Intelligence
Type-2 Fuzzy Logic: Theory and Applications
Type-2 Fuzzy Logic: Theory and Applications
Engineering Applications of Artificial Intelligence
Type-2 FLCs: A New Generation of Fuzzy Controllers
IEEE Computational Intelligence Magazine
Type-2 fuzzy sets and systems: an overview
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
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic 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
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network
International Journal of Fuzzy System Applications
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In this paper, a novel embedded real-time interval type-2 fuzzy neural network (FNN) system identification is presented using intelligent algorithms, back propagation (BP) algorithms. Interval type-2 FNN is introduced to handle uncertainties which arise from the noisy training data, noisy measurements used to activate the fuzzy logic system (FLS) and linguistic uncertainties. In order to overcome the iterative type-reduction overhead, the intelligent algorithms are proposed to learn the parameters of interval type-2 FLS using uncertainty bounds, inner- and outer-bound sets, which provide estimates of the uncertainties contained in the output of an interval type-2 FLS without having to perform the costly computations of type-reduction. Two nonlinear systems, namely, Duffing forced oscillation system and inverted pendulum system, are fully illustrated to be identified and simulation results show that not only similar identification performance to one that use type-reduction can be achieved but also significantly faster real-time identification can be performed.