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Mechanism for Measuring System Complexity Applying Sensitivity Analysis

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  • Viviane M. Gomes
  • Joao R. B. Paiva
  • Marcio R. C. Reis
  • Gabriel A. Wainer
  • Wesley P. Calixto

Abstract

This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.

Suggested Citation

  • Viviane M. Gomes & Joao R. B. Paiva & Marcio R. C. Reis & Gabriel A. Wainer & Wesley P. Calixto, 2019. "Mechanism for Measuring System Complexity Applying Sensitivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    • Handle: RePEc:hin:complx:1303241
    DOI: 10.1155/2019/1303241
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    References listed on IDEAS

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    1. Rosso, Osvaldo A. & Carpi, Laura C. & Saco, Patricia M. & Gómez Ravetti, Martín & Plastino, Angelo & Larrondo, Hilda A., 2012. "Causality and the entropy–complexity plane: Robustness and missing ordinal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 42-55.
    2. Sobol’, I.M. & Tarantola, S. & Gatelli, D. & Kucherenko, S.S. & Mauntz, W., 2007. "Estimating the approximation error when fixing unessential factors in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 957-960.
    3. Nam Ho Kim & Haoyu Wang & Nestor V. Queipo, 2006. "Adaptive reduction of random variables using global sensitivity in reliability-based optimisation," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 1(1/2), pages 102-119.
    4. Michael Batty & Robin Morphet & Paolo Masucci & Kiril Stanilov, 2014. "Entropy, complexity, and spatial information," Journal of Geographical Systems, Springer, vol. 16(4), pages 363-385, October.
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    Cited by:

    1. João R. B. Paiva & Alana S. Magalhães & Pedro H. F. Moraes & Júnio S. Bulhões & Wesley P. Calixto, 2021. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System," Energies, MDPI, vol. 14(22), pages 1-21, November.
    2. Amr Mahmoud & Mohamed Zohdy, 2022. "Dynamic Lyapunov Machine Learning Control of Nonlinear Magnetic Levitation System," Energies, MDPI, vol. 15(5), pages 1-16, March.

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