Title :
Radial basis function networks in nonlinear signal processing applications
Author :
Kassam, Saleem A. ; Cha, Inhyok
Author_Institution :
Dept. of Electr. Eng., Pennsylvania Univ., Philadelphia, PA, USA
Abstract :
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
Keywords :
feedforward neural nets; filtering and prediction theory; interference suppression; learning (artificial intelligence); signal processing; telecommunication channels; time series; channel equalization; interference cancellation; nonlinear filtering; nonlinear signal processing; radial basis function networks; stochastic error gradients; time-series prediction; training algorithm; Filtering; Intelligent networks; Interference cancellation; Multi-layer neural network; Multidimensional signal processing; Neural networks; Radial basis function networks; Signal processing; Signal processing algorithms; Stochastic processes;
Conference_Titel :
Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-4120-7
DOI :
10.1109/ACSSC.1993.342415
