A new approach using temporal radial basis function in chronological series

Abstract EN

In this paper, we present an extended form of the radial basis function network called temporal- radial basis function network.

This extended network is used in decision rules and classification in spatio-temporal domain applications like speech recognition, economic fluctuations, seismic measurements and robotics applications.

We found that such a network complies, with a relative ease, to constraints such as capacity of universal approximation, sensibility of node, local generalisation in receptive field, etc.

For an optimal solution based on a probabilistic approach with a minimum of complexity, we developped two temporal radial basis function models.

Application to the problem of Mackey-Glass time series, it has revealed that temporal radial basis function models are very promising, compared to traditional networks.