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- Romuald Boné, Michel Crucianu, Jean Pierre Asselin de Beauville
- Neurocomputing
- 2002

- Mohamed Slimane, Gilles Venturini, Jean Pierre Asselin de Beauville, Thierry Brouard, A. Brandeau
- Artificial Evolution
- 1995

When long-term dependencies are present in a time series, the approximation capabilities of recurrent neural networks are difficult to exploit by gradient descent algorithms. It is easier for such algorithms to find good solutions if one includes connections with time delays in the recurrent networks. One can choose the locations and delays for these… (More)

- Michel Crucianu, Romuald Boné, Jean Pierre Asselin de Beauville
- Neurocomputing
- 2001

We extend the Bayesian framework to Multi-Layer Perceptron models of Non-linear Auto-Regressive time-series. The approach is evaluated on an artificial time-series and some common simplifications are discussed.

Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies are difficult to learn if gradient descent algorithms are employed. We support the view that it is easier for these algorithms to find good solutions if one includes connections with… (More)

YANNS (Yet Another Neural Network Simulator) is a new object-oriented neural network simulator for feedforward networks as well as general recurrent networks. The goal of this project is to develop and implement a simulation tool that satisfies the following constraints: flexibility, ease of use, portability and efficiency. The result is a simulator with… (More)

sets of neural networks and we use the mean predictions. Also, we develop models on different training sets and test them on different tests sets before concluding that one class of models is superior to the others. We study the problem of generating monthly forecasts of the CAC 40 financial index. We compare the results obtained by linear models and… (More)