New developments in the theory and training of reformulated radial basis neural networks

Builds upon an axiomatic approach proposed for constructing reformulated radial basis function (RBF) neural networks suitable for gradient descent learning. This approach reduces the construction of RBF models to the selection of admissible generator functions. The selection of generator functions relies on criteria resulting from the analysis of the sensitivity of reformulated RBF models to gradient descent learning. The results of the study outlined in the paper are verified by a series of experiments on speech data