Training multilayer perceptrons with the extended Kalman algorithm
Advances in neural information processing systems 1
Local feedback multilayered networks
Neural Computation
A New IIR-MLP Learning Algorithm for On-Line Signal Processing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Diagrammatic derivation of gradient algorithms for neural networks
Neural Computation
A simplified gradient algorithm for iir synapse multilayer perceptrons
Neural Computation
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks
IEEE Transactions on Neural Networks
Locally recurrent globally feedforward networks: a critical review of architectures
IEEE Transactions on Neural Networks
Application of the recurrent multilayer perceptron in modeling complex process dynamics
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Short term wind power forecasting using time series neural networks
Proceedings of the 2011 Emerging M&S Applications in Industry and Academia Symposium
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
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The paper deals with a real-world application, the long-term wind speed and power forecasting in a wind farm using locally recurrent multilayer networks as forecast models. To cope with the complexity of the process and to improve the performance of the models, a class of optimal on-line learning algorithms is employed for training the locally recurrent networks based on the recursive prediction error (RPE) algorithm. A global RPE algorithm is devised and three local learning algorithms are suggested by partitioning the GRPE into a set of sub-problems at the neuron level to reduce computational complexity and storage requirements. Experimental results on the wind prediction problem demonstrate that the proposed algorithms exhibit enhanced performance, in terms of convergence speed and the accuracy of the attained solutions, compared to conventional gradient-based methods. Furthermore, it is shown that the suggested recurrent forecast models outperform the atmospheric and time-series models.