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
The magnitude of the diagonal elements in neural networks
Neural Networks
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Accelerating neural network training using weight extrapolations
Neural Networks
Additive neural networks and periodic patterns
Neural Networks
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
Radial Basis Function network learning using localized generalization error bound
Information Sciences: an International Journal
The evolutionary learning rule for system identification
Applied Soft Computing
Type-2 fuzzy logic controllers: a way forward for fuzzy systems in real world environments
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Uncertainty measures for general Type-2 fuzzy sets
Information Sciences: an International Journal
On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling
Information Sciences: an International Journal
A type-2 fuzzy wavelet neural network for time series prediction
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Expert Systems with Applications: An International Journal
Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
A review on the design and optimization of interval type-2 fuzzy controllers
Applied Soft Computing
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
Analysis of the back-propagation algorithm with momentum
IEEE Transactions on Neural Networks
eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System
Information Sciences: an International Journal
A new indirect approach to the type-2 fuzzy systems modeling and design
Information Sciences: an International Journal
| Hi-index | 0.07 |
In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the type-2 fuzzy weights by applying a different size of the footprint of uncertainty (FOU). The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a case of prediction for the Mackey-Glass time series (for @t=17). Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.