DocumentCode
771834
Title
Applying radial basis functions
Author
Mulgrew, Bernard
Author_Institution
Dept. of Electr. Eng., Edinburgh Univ., UK
Volume
13
Issue
2
fYear
1996
fDate
3/1/1996 12:00:00 AM
Firstpage
50
Lastpage
65
Abstract
Discusses the application of neural networks to general and radial basis functions and in particular to adaptive equalization and interference rejection problems. Neural-network-based algorithms strike a good balance between performance and complexity in adaptive equalization, and show promise in spread spectrum systems
Keywords
adaptive equalisers; cochannel interference; decision feedback equalisers; feedforward neural nets; recurrent neural nets; spread spectrum communication; telecommunication computing; Bayesian equalizers; RBF networks; adaptive equalization; co-channel interference; complexity; decision feedback equalizers; interference rejection; neural-network-based algorithms; performance; radial basis functions; recurrent networks; spread spectrum systems; training; Adaptive equalizers; Adaptive filters; Artificial neural networks; Bayesian methods; Bit error rate; Neural networks; Radial basis function networks; Signal processing; Signal processing algorithms; Spread spectrum communication;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/79.487041
Filename
487041
Link To Document