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Volume 2017, Article ID 8570720, 14 pages
Research Article

Tracking Nonlinear Correlation for Complex Dynamic Systems Using a Windowed Error Reduction Ratio Method

1School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
2School of Geography, University of Lincoln, Lincoln, UK
3Department of Geography, University of Sheffield, Sheffield, UK
4Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
5School of Optics and Electronics, Beijing Institute of Technology, Beijing, China

Correspondence should be addressed to Yifan Zhao; and Yitian Zhao; nc.ude.tib@oahz.naitiy

Received 23 June 2017; Revised 27 September 2017; Accepted 8 October 2017; Published 6 November 2017

Academic Editor: Daniela Paolotti

Copyright © 2017 Yifan Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Studying complex dynamic systems is usually very challenging due to limited prior knowledge and high complexity of relationships between interconnected components. Current methods either are like a “black box” that is difficult to understand and relate back to the underlying system or have limited universality and applicability due to too many assumptions. This paper proposes a time-varying Nonlinear Finite Impulse Response model to estimate the multiple features of correlation among measurements including direction, strength, significance, latency, correlation type, and nonlinearity. The dynamic behaviours of correlation are tracked through a sliding window approach based on the Blackman window rather than the simple truncation by a Rectangular window. This method is particularly useful for a system that has very little prior knowledge and the interaction between measurements is nonlinear, time-varying, rapidly changing, or of short duration. Simulation results suggest that the proposed tracking approach significantly reduces the sensitivity of correlation estimation against the window size. Such a method will improve the applicability and robustness of correlation analysis for complex systems. A real application to environmental changing data demonstrates the potential of the proposed method by revealing and characterising hidden information contained within measurements, which is usually “invisible” for conventional methods.