Reformulated radial basis neural networks with adjustable weighted norms

Introduces reformulated radial basis function (RBF) neural networks with adjustable weighted norms. This work extends and improves an axiomatic approach that reduced the construction of RBF models to the selection of admissible generator functions. The RBF models proposed in this paper are constructed by linear generator functions and employ weighted norms that can be updated during learning to facilitate the implementation of the desired input-output mapping. Experiments on speech data verify that the proposed RBF models outperform conventional RBF neural networks with Gaussian radial basis functions and reformulated RBF neural networks constructed by linear generator functions and employing fixed Euclidean norms