论文信息 - Radial basis function networks in nonlinear signal processing applications

Radial basis function networks in nonlinear signal processing applications

We consider radial basis function networks (RBFN) for use in various nonlinear signal processing applications. We first present a simple training algorithm for the RBFN based on stochastic gradients of error. We then demonstrate and discuss the usefulness of RBFNs in various applications including channel equalization, interference cancellation, time-series prediction, and nonlinear filtering.<<ETX>>

Saleem A. Kassam | Inhyok Cha

[1] Sheng Chen,et al. Reconstruction of binary signals using an adaptive radial-basis-function equalizer , 1991, Signal Process..

[2] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[3] Saleem A. Kassam,et al. Channel Equalization Using Adaptive Complex Radial Basis Function Networks , 1995, IEEE J. Sel. Areas Commun..

[4] C.F.N. Cowan,et al. Adaptive equalization of finite nonlinear channels using multilayer perceptron , 1990 .

[5] Saleem A. Kassam,et al. Interference cancellation using radial basis function networks , 1992, [1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing.