Tracking nonlinear correlation for complex dynamic systems using a Windowed Error Reduction Ratio method

Zhao, Yifan and Hanna, Edward and Bigg, Edward and Zhao, Yitian (2017) Tracking nonlinear correlation for complex dynamic systems using a Windowed Error Reduction Ratio method. Complexity, 2017 . p. 8570720. ISSN 1076-2787

Full content URL: https://doi.org/10.1155/2017/8570720

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Abstract

Studying complex dynamic systems is usually very challenging due to limited prior knowledge and high complexity of relationships between interconnected components. Current methods are either 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 to better understand complex systems by revealing and characterising hidden information contained within measurements, which is usually ‘invisible’ for conventional methods.

Keywords:Nonlinearities, NARMAX, tracking system, delay analysis, MIMO
Subjects:G Mathematical and Computer Sciences > G340 Statistical Modelling
Divisions:College of Science > School of Geography
ID Code:29037
Deposited On:11 Nov 2017 12:44

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