The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A tutorial on support vector regression
Statistics and Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
An approach to online identification of Takagi-Sugeno fuzzy models
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
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition
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
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine
IEEE Transactions on Fuzzy Systems
Fuzzy Regression Analysis by Support Vector Learning Approach
IEEE Transactions on Fuzzy Systems
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Blind Image Deconvolution Through Support Vector Regression
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A New Solution Path Algorithm in Support Vector Regression
IEEE Transactions on Neural Networks
Recurrent fuzzy system design using elite-guided continuous ant colony optimization
Applied Soft Computing
An EEG-based brain-computer interface for dual task driving detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
TS-fuzzy modeling based on ε-insensitive smooth support vector regression
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper proposes a new recurrent model, known as the locally recurrent fuzzy neural network with support vector regression (LRFNN-SVR), that handles problems with temporal properties. Structurally, an LRFNN-SVR is a five-layered recurrent network. The recurrent structure in an LRFNN-SVR comes from locally feeding the firing strength of each fuzzy rule back to itself. The consequent layer in an LRFNN-SVR is a Takagi-Sugeno-Kang (T-S-K)-type consequent, which is a linear function of current states, regardless of system input and output delays. For the structure learning, a one-pass clustering algorithm clusters the input-training data and determines the number of network nodes in hidden layers. For the parameter learning, an iterative linear SVR algorithm is proposed to tune free parameters in the rule consequent part and feedback loops. The motivation for using SVR for parameter learning is to improve the LRFNN-SVR generalization ability. This paper demonstrates LRFNN-SVR capabilities by conducting simulations in dynamic system prediction and identification problems with noiseless and noisy data. In addition, this paper compares simulation results from the LRFNN-SVR with other recurrent fuzzy models.