Rational Function Neural Network
- 1 November 1993
- journal article
- Published by MIT Press in Neural Computation
- Vol. 5 (6) , 928-938
- https://doi.org/10.1162/neco.1993.5.6.928
Abstract
In this paper we observe that a particular class of rational function (RF) approximations may be viewed as feedforward networks. Like the radial basis function (RBF) network, the training of the RF network may be performed using a linear adaptive filtering algorithm. We illustrate the application of the RF network by considering two nonlinear signal processing problems. The first problem concerns the one-step prediction of a time series consisting of a pair of complex sinusoid in the presence of colored non-gaussian noise. Simulated data were used for this problem. In the second problem, we use the RF network to build a nonlinear dynamic model of sea clutter (radar backscattering from a sea surface); here, real-life data were used for the study.Keywords
This publication has 7 references indexed in Scilit:
- Networks for approximation and learningProceedings of the IEEE, 1990
- Networks and the best approximation propertyBiological Cybernetics, 1990
- The fast adaptive ROTOR's RLS algorithmIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Is there a radar clutter attractor?Applied Physics Letters, 1990
- Parameter estimation of exponentially damped sinusoids using higher order statisticsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Nonlinear prediction of chaotic time seriesPhysica D: Nonlinear Phenomena, 1989
- On the approximate realization of continuous mappings by neural networksNeural Networks, 1989