Dispersive networks for nonlinear adaptive filters

The authors describe a dispersive network architecture that can be used for nonlinear adaptive channel equalization and signal prediction. Dispersive networks contain internal delay elements that spread out features in the input signal over time and space, so that they influence the output at multiple points in the future. When used for equalization, these networks can compensate for nonlinear channel distortions and achieve a lower error than conventional backpropagation networks of comparable size. In a signal prediction task, dispersive networks can adapt and predict simultaneously in an online environment, while conventional backpropagation networks require additional hardware.<<ETX>>

[1]  David J. Burr,et al.  Experiments on neural net recognition of spoken and written text , 1988, IEEE Trans. Acoust. Speech Signal Process..

[2]  Eric A. Wan,et al.  Temporal backpropagation for FIR neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[3]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[4]  S. P. Day,et al.  Continuous-time temporal back-propagation , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[5]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[6]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[7]  Dennis Gabor,et al.  A universal nonlinear filter, predictor and simulator which optimizes itself by a learning process , 1961 .

[8]  Michael R. Davenport,et al.  Continuous-time temporal back-propagation with adaptable time delays , 1993, IEEE Trans. Neural Networks.

[9]  J. D. Farmer,et al.  Chaotic attractors of an infinite-dimensional dynamical system , 1982 .

[10]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[11]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

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

[13]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[14]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[15]  W.C. Mead,et al.  Using CNLS-net to predict the Mackey-Glass chaotic time series , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.