Nonlinear channel equalization using new neural network model

Author

Kim, Yong-Woon ; Park, Dong-Jo

Author_Institution

Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea

Volume

2

fYear

1999

fDate

36342

Firstpage

827

Abstract

A generalized diagonal recurrent neural network (GDRNN) and its learning algorithm for dynamic systems are proposed. The hidden nodes of the GDRNN have recurrent weights to capture the dynamic characteristics of the given nonlinear systems. The GDRNN with the proposed learning algorithm gives faster learning speed and better convergence properties than the conventional diagonal recurrent neural network (DRNN). In computer simulations, the performance of the GDRNN is compared with that of the conventional neural network

Keywords

convergence; equalisers; learning (artificial intelligence); nonlinear systems; recurrent neural nets; telecommunication channels; GDRNN; convergence; dynamic systems; generalized diagonal recurrent neural network; hidden nodes; learning algorithm; learning speed; neural network model; nonlinear channel equalization; nonlinear systems; Computer simulation; Delay; Feedback loop; Feedforward neural networks; Neural networks; Neurofeedback; Neurons; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831058

Filename

831058