Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation

摘要：

　　To recover QAM signals at the receiver of blind equalizer,a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed.The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate,and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance.The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error.The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

关键词： signal transformation fuzzy C-means clustering algorithm fuzzy neural network underwater acoustic channel

作者: Yecai Guo Zhengxin Liu

作者单位: College of Electronic and Information Engineering, Nanjing University of Information Science and Tec School of Electrical Engineering and Information, Anhui University of Science and Technology, Huaina

会议类型: 国际会议

会议名称: the 2010 International Conference on Frontiers of Manufacturing and Design Science(第一届制造与设计科学国际会议(ICFMD 2010))

会议地点: 重庆

会议语种:英文

页码: 4146-4150

在线出版日期: 2010-12-11（万方平台首次上网日期，不代表论文的发表时间）