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Generating discrete dynamical system equations from input–output data using neural network identification models

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  • Maroli, John M.

Abstract

This research presents a novel framework for generating equations describing discrete dynamical systems from only input–output data. The framework operates in two steps, creating a system identification model from input–output data using neural networks and then performing sensitivity analysis on the model. The sensitivity analysis is driven by a uniquely constrained functional decomposition of the identification model that breaks a complex identification problem into a group of small curve fitting problems. The resultant system equation represents the neural network identification model and by proxy the original system from which the input–output data belongs. The analysis allows for system equations to be generated from both black box systems and identification models, which can then be used for transparent and interpretable replacement of opaque system models. Transparent models can be better understood, leading to increased trustworthiness, safety, and reliability. An open source code implementation of the framework is created and made publicly available.

Suggested Citation

  • Maroli, John M., 2023. "Generating discrete dynamical system equations from input–output data using neural network identification models," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001138
    DOI: 10.1016/j.ress.2023.109198
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    References listed on IDEAS

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    1. Wang, Zhenqiang & Jia, Gaofeng, 2023. "Extended sample-based approach for efficient sensitivity analysis of group of random variables," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Kapusuzoglu, Berkcan & Mahadevan, Sankaran, 2021. "Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Becker William & Paruolo Paolo & Saltelli Andrea, 2021. "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 187-233, July.
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    Cited by:

    1. Meshach Kumar & Utkal Mehta & Giansalvo Cirrincione, 2023. "A Novel Approach to Modeling Incommensurate Fractional Order Systems Using Fractional Neural Networks," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    2. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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