Cosine radial basis function neural networks

This paper introduces a new family of reformulated radial basis function (RBF) neural networks, which are referred to as cosine RBFs. These RBF models are developed by relaying upon an axiomatic approach proposed for constructing reformulated RBF neural networks suitable for gradient descent learning. This approach reduces the search for RBF models to the selection of admissible generator functions. Cosine RBF models are constructed by linear generator functions of a special form. A set of experiments on two data sets indicate that cosine RBFs outperform considerably conventional RBF neural networks with Gaussian radial basis functions. Cosine RBFs are also strong competitors to existing reformulated RBF models trained by gradient descent and conventional feedforward neural networks with sigmoid hidden units.