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

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. 2011 Elsevier Ltd. All rights reserved.

authors

• Bukkapatnam, Satish

published proceedings

• PATTERN RECOGNITION

author list (cited authors)

• Yang, H., Bukkapatnam, S., & Barajas, L. G.

citation count

• 26

complete list of authors

• Yang, Hui||Bukkapatnam, Satish TS||Barajas, Leandro G

publication date

• January 2011

publisher

• Elsevier  Publisher

published in

• Pattern Recognition  Journal

keywords

• Nonstationary
• Prediction
• Recurrence Plot
• Time Series

Digital Object Identifier (DOI)

• 10.1016/j.patcog.2011.01.010

start page

• 1834

end page

• 1840

volume

• 44

issue

• 8

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.patcog.2011.01.010