A class of improved least sum of exponentials algorithms

Shiyuan Wang, Yunfei Zheng, Shukai Duan, Lidan Wang, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)340-349
Number of pages10
JournalSignal Processing
Publication statusPublished - 1 Nov 2016


  • Energy conservation relation
  • Kernel method
  • Least sum of exponentials
  • Variable scaling factor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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