Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
405564 | Neural Networks | 2011 | 7 Pages |
Abstract
To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Haiquan Zhao, Xiangping Zeng, Jiashu Zhang, Yangguang Liu, Xiaomin Wang, Tianrui Li,