Nonlinear channel equalization using new neural network model
Yong‐Woon Kim, Dong-Jo Park · 2003
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.