Computer
Structure identification of fuzzy model
Fuzzy Sets and Systems
Neurocomputations in Relational Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Neural network design
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Knowledge Representation in Fuzzy Logic
IEEE Transactions on Knowledge and Data Engineering
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Information Sciences: an International Journal
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Information Sciences: an International Journal
Uncertainty measures for interval type-2 fuzzy sets
Information Sciences: an International Journal
Design of interval type-2 fuzzy sliding-mode controller
Information Sciences: an International Journal
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Fuzzy-set based models of neurons and knowledge-based networks
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
Hierarchical type-2 neuro-fuzzy BSP model
Information Sciences: an International Journal
Robustness of interval-valued fuzzy inference
Information Sciences: an International Journal
Design of interval type-2 fuzzy models through optimal granularity allocation
Applied Soft Computing
Information Sciences: an International Journal
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
The sampling method of defuzzification for type-2 fuzzy sets: Experimental evaluation
Information Sciences: an International Journal
A review on the design and optimization of interval type-2 fuzzy controllers
Applied Soft Computing
Type-2 neuro-fuzzy modeling for a batch biotechnological process
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
A complex social system simulation using type-2 fuzzy logic and multiagent system
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review
Information Sciences: an International Journal
Interval type 2 hierarchical FNN with the H-infinity condition for MIMO non-affine systems
Applied Soft Computing
Algebraic structures of interval-valued fuzzy ( S,N)-implications
International Journal of Approximate Reasoning
On interval type-2 rough fuzzy sets
Knowledge-Based Systems
On characterization of generalized interval type-2 fuzzy rough sets
Information Sciences: an International Journal
Complete solution sets of inf → interval-valued fuzzy relation equations
Information Sciences: an International Journal
Information Sciences: an International Journal
A 2uFunction representation for non-uniform type-2 fuzzy sets: Theory and design
International Journal of Approximate Reasoning
System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network
International Journal of Fuzzy System Applications
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Predictable type-2 fuzzy mobile units for energy balancing in wireless sensor networks
Information Sciences: an International Journal
Bifurcating fuzzy sets: Theory and application
Neurocomputing
Advances in Fuzzy Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
International Journal of Hybrid Intelligent Systems
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In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule's antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANFIS is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems.