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

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...

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Bibliographic Details
Published in:Pattern recognition 2011-08, Vol.44 (8), p.1834-1840
Main Authors: Yang, Hui, Bukkapatnam, Satish T.S., Barajas, Leandro G.
Format: Article
Language:English
Subjects:
Applied sciences
Dynamic tests
Dynamical systems
Exact sciences and technology
Information, signal and communications theory
Inventory control, production control. Distribution
Mathematical analysis
Mathematical models
Miscellaneous
Nonlinear dynamics
Nonlinearity
Nonstationary
Operational research and scientific management
Operational research. Management science
Performance prediction
Prediction
Recurrence plot
Segments
Series (mathematics)
Signal processing
Telecommunications and information theory
Time series
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Summary: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. ► We make predictions in the nonlinear system under highly nonstationary conditions. ► Recurrence patterns are used to partition state space into near-stationary segments. ► We utilize local recurrence pattern recognition approach for prediction purposes. ► Local recurrence model captures both nonlinear dynamics and nonstationarity. ► Experiments show the superiority of local recurrence model over other alternatives.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2011.01.010