Local feedback multilayered networks
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
A recurrent fuzzy-neural model for dynamic system identification
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
Noisy speech processing by recurrently adaptive fuzzy filters
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
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
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Neural networks designed on approximate reasoning architecture and their applications
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Locally recurrent globally feedforward networks: a critical review of architectures
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Prediction error feedback for time series prediction: a way to improve the accuracy of predictions
ECC'10 Proceedings of the 4th conference on European computing conference
WSEAS Transactions on Systems and Control
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In this paper, a locally feedback dynamic fuzzy neural network (LF-DFNN) for modeling of temporal processes is suggested. The model is composed of dynamic TSK-type fuzzy rules where the consequent sub-models are implemented by recurrent neural networks with internal feedback paths and dynamic neuron synapses. The LF-DFNN exhibits some interesting features, such as enhanced representation power, local modeling characteristics, model parsimony, and stable learning. Training of the LF-DFNN models is achieved using an optimal on-line learning scheme, the decoupled recursive prediction error algorithm (DRPE). The method has reduced computational demands and is derived through decomposition of the weight vector to several mutually exclusive weight groups. The partial derivatives required for the implementation of the training algorithm are calculated using the adjoint model approach, adapted to the fuzzy network's architecture exercised here. The paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. The LF-DFNN networks are used as advanced forecast models, providing multi-step ahead wind speed estimates from 15min to 3h ahead. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that DRPE outperforms three gradient descent algorithms, in training of the recurrent forecast models.