Convergence problems due to an RBF classifier's learning process

摘要

In this paper, we present the architecture of an RBF neural classifier. We show that a global learning algorithm concentrating only on the centres, the Gaussian widths and the weights of the connections is inadequate for this architecture. Then, we propose to use an hybrid learning algorithm in which the Gaussian centres are first fixed. This one gives satisfactory results.