Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Hui Yang, Satish T.S. Bukkapatnam, Leandro G. Barajas

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

This paper presents a local recurrence modeling approach for state and performance predictions in complex nonlinear and nonstationary systems. Nonstationarity is treated as the switching force between different stationary systems, which is shown as a series of finite time detours of system dynamics from the vicinity of a nonlinear attractor. Recurrence patterns are used to partition the system trajectory into multiple near-stationary segments. Consequently, piecewise eigen analysis of ensembles in each near-stationary segment can capture both nonlinear stochastic dynamics and nonstationarity. The experimental studies using simulated and real-world datasets demonstrate significant prediction performance improvements in comparison with other alternative methods.

Original languageEnglish (US)
Pages (from-to)1834-1840
Number of pages7
JournalPattern Recognition
Volume44
Issue number8
DOIs
StatePublished - Aug 1 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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