Least squares-based iterative identification methods for linear-in-parameters systems using the decomposition technique

Feifei Wang, Yanjun Liu, Erfu Yang

Research output: Contribution to journalArticle

5 Citations (Scopus)
96 Downloads (Pure)

Abstract

By extending the least squares-based iterative (LSI) method, this paper presents a decomposition-based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an interval-varying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious sub-systems and then to derive new iterative algorithms to estimate the parameters of each sub-system. Compared with the LSI algorithm and the interval-varying LSI algorithm, the decomposition-based iterative algorithms have less computational load. The numerical simulation results demonstrate that the proposed algorithms work quite well.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalCircuits, Systems, and Signal Processing
Early online date6 Jan 2016
DOIs
Publication statusE-pub ahead of print - 6 Jan 2016

Keywords

  • parameter estimation
  • iterative identification
  • decomposition techniques
  • missing data
  • linear-in-parameters system

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