TY - JOUR
T1 - A class of improved least sum of exponentials algorithms
AU - Wang, Shiyuan
AU - Zheng, Yunfei
AU - Duan, Shukai
AU - Wang, Lidan
AU - Tse, Chi K.
PY - 2016/11
Y1 - 2016/11
N2 - A class of improved least sum of exponentials (ILSE) algorithms is proposed by incorporating a scaling factor into the cost function of LSE in this paper. The even-order moment information regarding error is influenced by the scaling factor. However, the ILSE algorithm based on a fixed scaling factor can only provide a tradeoff between the convergence rate and steady-state excess-mean-square error (EMSE). Therefore, a variable scaling factor ILSE (VS-ILSE) algorithm is also proposed to improve the convergence rate and steady-state EMSE, simultaneously. To facilitate analysis, the energy conservation relation of ILSE is established, providing a sufficient condition for mean square convergence and a theoretical value of the steady-state EMSE. In addition, the kernel extensions of ILSE and VS-ILSE are further developed for performance improvement. Simulation results illustrate the theoretical analysis and the excellent performance of the proposed methods.
AB - A class of improved least sum of exponentials (ILSE) algorithms is proposed by incorporating a scaling factor into the cost function of LSE in this paper. The even-order moment information regarding error is influenced by the scaling factor. However, the ILSE algorithm based on a fixed scaling factor can only provide a tradeoff between the convergence rate and steady-state excess-mean-square error (EMSE). Therefore, a variable scaling factor ILSE (VS-ILSE) algorithm is also proposed to improve the convergence rate and steady-state EMSE, simultaneously. To facilitate analysis, the energy conservation relation of ILSE is established, providing a sufficient condition for mean square convergence and a theoretical value of the steady-state EMSE. In addition, the kernel extensions of ILSE and VS-ILSE are further developed for performance improvement. Simulation results illustrate the theoretical analysis and the excellent performance of the proposed methods.
KW - Energy conservation relation
KW - Kernel method
KW - Least sum of exponentials
KW - Variable scaling factor
UR - http://www.scopus.com/inward/record.url?scp=84973352657&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84973352657&origin=recordpage
U2 - 10.1016/j.sigpro.2016.05.005
DO - 10.1016/j.sigpro.2016.05.005
M3 - RGC 21 - Publication in refereed journal
SN - 0165-1684
VL - 128
SP - 340
EP - 349
JO - Signal Processing
JF - Signal Processing
ER -