SIGNED LEAST MEAN KURTOSIS-BASED ADAPTIVE LINE ENHANCER

摘要：

Based on the aim of the characteristic of error kurtosis and signed-error, a novel algorithm of sign least mean kurtosis based adaptive line enhancer (SLMKBALE) is proposed.Simulation results have shown that the computational load of the proposed SLMKBALE algorithm is much lower than that of the LMKBALE(least mean kurtosis based adaptive line enhancer) and as many as that of LMSBALE(least mean square based adaptive line enhancer), and the SLMKBALE algorithm has better ability to hand non-Gaussian and enhancing signal spectrum in comparison with the LMSBALE, SLMSBALE(signed LMSBALE), LMFBALE (least mean fourth based adaptive line enhancer) and LMK- BALE algorithm and that the mean square error(MSE) of the proposed algorithm is the lowest in all algorithms when the MSE converges. Therefore, the SLMKBALE algorithm is useful and reliable.

关键词： Error kurtosis Sign least mean kurtosis (SLMK) Convergence Computational load

作者: JI-CHENG LING LONG-QING HE YE-CAI GUO

作者单位: Nanjing XIaozhuang College, Nanjing 210017, China Anhui University of Science and Technology, Huainan 232001, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 278-282

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）