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Training recurrent neural networks: why and how? An illustration in dynamical process modeling.

, , , , and . IEEE Trans. Neural Networks, 5 (2): 178-184 (1994)

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MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling., and . J. Mach. Learn. Res., (2003)Pairwise Neural Network Classifiers with Probabilistic Outputs, , , and . Advances in Neural Information Processing Systems 7 (NIPS-94), page 1109--1116. MIT Press, (1995)Undersampling for the training of feedback neural networks on large sequences; application to the modeling of an induction machine., , , and . ICECS, page 1025-1028. IEEE, (1999)High Order Neural Networks for Efficient Associative Memory Design., , , and . NIPS, page 233-241. American Institue of Physics, (1987)Single-layer learning revisited: a stepwise procedure for building and training a neural network., , and . NATO Neurocomputing, volume 68 of NATO ASI Series, page 41-50. Springer, (1989)Single-Layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network, , and . Neurocomputing: Algorithms, Architectures and Applications, volume F68 of NATO ASI Series, Springer-Verlag, (1990)Handwritten Digit Recognition by Neural Networks with Single-Layer Training, , and . IEEE Transactions on Neural Networks, 3 (6): 962--968 (1992)Pairwise Neural Network Classifiers with Probabilistic Outputs., , , and . NIPS, page 1109-1116. MIT Press, (1994)Neural Network Models and Applications: an Overview., and . Modelling the Innovation, page 1-8. North-Holland, (1990)A Formal Model of the Insect Olfactory Macroglomerulus: Simulations and Analytic Results., , , , , and . NIPS, page 1022-1029. Morgan Kaufmann, (1992)