  In this paper, we propose a novel adaptive kernel for the radial basis
function (RBF) neural networks. The proposed kernel adaptively fuses the
Euclidean and cosine distance measures to exploit the reciprocating properties
of the two. The proposed framework dynamically adapts the weights of the
participating kernels using the gradient descent method thereby alleviating the
need for predetermined weights. The proposed method is shown to outperform the
manual fusion of the kernels on three major problems of estimation namely
nonlinear system identification, pattern classification and function
approximation.
