Local feedback multilayered networks
Neural Computation
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Diagrammatic derivation of gradient algorithms for neural networks
Neural Computation
Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks
Information Sciences: an International Journal
Information Sciences: an International Journal
Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
Information Sciences: an International Journal
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Genetically optimized fuzzy polynomial neural networks
IEEE Transactions on Fuzzy Systems
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
On the choice of parameters of the cost function in nested modular RNN's
IEEE Transactions on Neural Networks
A stable neural network-based observer with application to flexible-joint manipulators
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
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling
Information Sciences: an International Journal
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base
Expert Systems with Applications: An International Journal
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Short term wind speed forecasting with evolved neural networks
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Information Sciences: an International Journal
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This paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. Owing to the temporal complexity of the problem, we employ local recurrent neural networks with internal dynamics, as advanced forecast models. To improve the prediction performance, the training task is accomplished using on-line learning algorithms based on the recursive prediction error (RPE) approach. A global RPE (GRPE) learning scheme is first developed where all adjustable weights are simultaneously updated. In the following, through weight grouping we devise a simplified method, the decoupled RPE (DRPE), with reduced computational demands. The partial derivatives required by the learning algorithms are derived using the adjoint model approach, adapted to the architecture of the networks being used. The efficiency of the proposed approach is tested on a real-world wind farm problem, where multi-step ahead wind speed estimates from 15min to 3h are sought. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that the suggested learning algorithms outperform three gradient descent algorithms, in training of the recurrent forecast models.