Center reduction algorithm for the modified probabilistic neural network equalizer

Author

Young, James P. ; Zaknich, Anthony ; Attkiouzel, Y.

Author_Institution

Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia

Volume

3

fYear

2001

fDate

2001

Firstpage

1966

Abstract

The applicability of the modified probabilistic neural network to channel equalization can be severely limited by the size of the network. The size of the network grows exponentially with the order of the channel and the dimension of the input vectors. As a result, the standard network is practical only for low order channels with small input alphabet size. An algorithm is proposed to alleviate such an undesirable constraint by finding a much smaller network representation with a similar decision surface

Keywords

digital communication; equalisers; learning (artificial intelligence); neural nets; telecommunication channels; telecommunication computing; center reduction algorithm; channel equalization; clustering; digital communication; equalizer; learning; probabilistic neural network; probability; Adaptive filters; Bayesian methods; Bit error rate; Convergence; Digital communication; Digital filters; Equalizers; Neural networks; Nonlinear filters; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938465

Filename

938465