A practical radial basis function equalizer

A radial basis function (RBF) equalizer design process has been developed in which the number of basis function centers used is substantially fewer than conventionally required. The reduction of centers is accomplished in two-steps. First an algorithm is used to select a reduced set of centers that lie close to the decision boundary. Then the centers in this reduced set are grouped, and an average position is chosen to represent each group. Channel order and delay, which are determining factors in setting the initial number of centers, are estimated from regression analysis. In simulation studies, an RBF equalizer with more than 2000-to-1 reduction in centers performed as well as the RBF equalizer without reduction in centers, and better than a conventional linear equalizer.

[1]  Sheng Chen,et al.  A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[2]  J. G. Proakis,et al.  Adaptive equalization with neural networks: new multi-layer perceptron structures and their evaluation , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

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

[5]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[6]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[7]  Sammy Siu,et al.  Multilayer perceptron structures applied to adaptive equalisers for data communications , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[8]  Sheng Chen,et al.  Adaptive Equalisation to finite Non-linear Channels using Multilayer Perceptrons , 1990 .

[9]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[10]  H. M. Hafez,et al.  Neural network channel equalization , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[11]  John G. Proakis,et al.  Digital Communications , 1983 .

[12]  Olli Simula,et al.  Combining linear equalization and self-organizing adaptation in dynamic discrete-signal detection , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .