The paper first summarizes a general approach to the training of recurrent neural networks by gradient-based algorithms, which leads to the introduction of four families of training algorithms. Because of the variety of possibilities thus available to the "neural network designer," the choice of the appropriate algorithm to solve a given problem becomes critical. We show that, in the case of process modeling, this choice depends on how noise interferes with the process to be modeled; this is evidenced by three examples of modeling of dynamical processes, where the detrimental effect of inappropriate training algorithms on the prediction error made by the network is clearly demonstrated. >
['O. Nerrand', 'P. Roussel-Ragot', 'D. Urbani', 'L. Personnaz', 'Gérard Dreyfus']
Training recurrent neural networks: why and how? An illustration in dynamical process modeling