Neural Processing Letters
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Measures for the organization of self-organizing maps
Self-Organizing neural networks
Hill-Climbing, Density-Based Clustering and Equiprobabilistic Topographic Maps
Journal of VLSI Signal Processing Systems
| Hi-index | 0.00 |
In this paper we observe that a particular class of rationalfunction (RF) approximations may be viewed as feedforward networks.Like the radial basis function (RBF) network, the training of theRF network may be performed using a linear adaptive filteringalgorithm. We illustrate the application of the RF network byconsidering two nonlinear signal processing problems. The firstproblem concerns the one-step prediction of a time seriesconsisting of a pair of complex sinusoid in the presence of colorednon-gaussian noise. Simulated data were used for this problem. Inthe second problem, we use the RF network to build a nonlineardynamic model of sea clutter (radar backscattering from a seasurface); here, real-life data were used for the study.