Multilayer feedforward networks are universal approximators
Neural Networks
Fuzzy engineering
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Model-based recurrent neural network for modeling nonlinear dynamicsystems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A GA-based method for constructing fuzzy systems directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction and identification using wavelet-based recurrent fuzzy neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Incremental learning of dynamic fuzzy neural networks for accurate system modeling
Fuzzy Sets and Systems
Improving Training in the Vicinity of Temporary Minima
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Qualitative modeling of dynamical systems employing continuous-time recurrent fuzzy systems
Fuzzy Sets and Systems
Intelligent forecasting of S&P 500 time series: a self-organizing fuzzy approach
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Modeling with discrete-time recurrent fuzzy systems via mixed-integer optimization
Fuzzy Sets and Systems
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A recurrent neuro-fuzzy approach with RO-LSE hybrid learning algorithm to the problem of system modeling is proposed in the paper. The proposed recurrent neuro-fuzzy system possesses six layers of neural network to perform the fuzzy inference. The recurrent structure is formed using lagged membership-grade signals as internal feedbacks to the layer of membership functions of fuzzy sets, and it is expected having great potential to trace the temporal change of signals. Fuzzy sets with time-varying kernels have excellent property, with that the input-output mapping of the neuro-fuzzy system is no longer fixed but time varying. In this study, a new parameter learning approach is proposed for NFS with good learning convergence, in which the hybrid RO-LSE learning algorithm is utilized for the update of parameters. The well-known random optimization (RO) method is used to update the parameters of the premise parts of the proposed system, and the method of least square estimation (LSE) to update those of the consequent parts. The hybrid algorithm is found useful, and it has shown fast convergence of parameter learning for the proposed system. Three examples are used to demonstrate the brilliancy of the proposed approach. Excellent performance of the proposed approach in modeling accuracy and learning convergence is observed.