Hyper Radial Basis Function Neural Network for Nonlinear Interference Cancellation
نویسنده
چکیده
In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GRBF) networks. This fact is important for our application and allow to achieve better results in nonlinear interference cancellation due to exible approximation properties of HRBF network. The simulation results and comparison with standard RBF network applying for interference cancellation are discussed. Validity, eeec-tiveness and advantages of HRBF network based cancellation system are demonstrated. This paper has not been submitted elsewhere in identical or similar form.
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Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal
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