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• W2012251008 abstract "Vorobyov, S. A., and Cichocki, A., Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal, Digital Signal Processing11 (2001) 204–221 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 is due to the capabilities of the HRBF networks to approximate arbitrary multidimensional nonlinear functions and better flexibility in comparison to RBF networks. Simulation examples and comparisons to the FIR-based linear canceller and the RBFN-based canceller demonstrate the usefulness and effectiveness of the HRBFN based canceller." @default.
• W2012251008 created "2016-06-24" @default.
• W2012251008 creator A5018676117 @default.
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• W2012251008 date "2001-07-01" @default.
• W2012251008 modified "2023-09-26" @default.
• W2012251008 title "Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal" @default.
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• W2012251008 doi "https://doi.org/10.1006/dspr.2001.0398" @default.
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