A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is
Efficient interference cancellation often requires nonlinear processing of a reference signal. In this paper, hyper radial basis function (HRBF) neural networks for adaptive interference cancellation is developed. We show that the HRBF networks, with an appropriate learning algorithm, is able to approximate the interference signal more efficiently than standard radial basis function (RBF) networks. The HRBF network-based canceller achieves better results for interference cancellation. This isdoi:10.1016/b978-0-7506-7444-7.x5036-5 fatcat:a73xdiqoifaohbn2gkk6crud64