Title :
Applying radial basis functions
Author :
Mulgrew, Bernard
Author_Institution :
Dept. of Electr. Eng., Edinburgh Univ., UK
fDate :
3/1/1996 12:00:00 AM
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;
Journal_Title :
Signal Processing Magazine, IEEE
