IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v235y2023ics0951832023001138.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023001138
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109198?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shang, Xiaobing & Wang, Lipeng & Fang, Hai & Lu, Lingyun & Zhang, Zhi, 2024. "Active Learning of Ensemble Polynomial Chaos Expansion Method for Global Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Zhao, Zhao & Lu, Zhao-Hui & Zhao, Yan-Gang, 2024. "A novel single-loop estimation method for predictive failure probability-based global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Moglen, Rachel L. & Barth, Julius & Gupta, Shagun & Kawai, Eiji & Klise, Katherine & Leibowicz, Benjamin D., 2023. "A nexus approach to infrastructure resilience planning under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Jiang, Fengyuan & Dong, Sheng, 2024. "Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Cao, Bohan & Yin, Qishuai & Guo, Yingying & Yang, Jin & Zhang, Laibin & Wang, Zhenquan & Tyagi, Mayank & Sun, Ting & Zhou, Xu, 2023. "Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Zhao, Xiang & Li, Hong-Shuang & Zhao, Zhen-Zhou & Xu, Chang, 2024. "Reliability-oriented global sensitivity analysis using subset simulation and space partition," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Grabill, Nicholas & Wang, Stephanie & Olayinka, Hammed A. & De Alwis, Tharindu P. & Khalil, Yehia F. & Zou, Jian, 2024. "AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    11. Zhao, Yunjie & Cheng, Xi & Zhang, Taihong & Wang, Lei & Shao, Wei & Wiart, Joe, 2023. "A global–local attention network for uncertainty analysis of ground penetrating radar modeling," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Ding, Jiayi & Zhou, Jianfang & Cai, Wei, 2023. "An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    13. Xiong, Qingwen & Du, Peng & Deng, Jian & Huang, Daishun & Song, Gongle & Qian, Libo & Wu, Zenghui & Luo, Yuejian, 2022. "Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    14. Yao, Wen & Zheng, Xiaohu & Zhang, Jun & Wang, Ning & Tang, Guijian, 2023. "Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    15. Chen, Jie & Yu, Yang & Liu, Yongming, 2022. "Physics-guided mixture density networks for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Lu, Ning & Li, Yan-Feng & Mi, Jinhua & Huang, Hong-Zhong, 2024. "AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    17. Zha, Congyi & Pan, Chenrong & Sun, Zhili & Liu, Qin, 2024. "A single-loop reliability sensitivity analysis strategy for time-dependent rare events with both random variables and stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    18. Qin, Zhiyuan & Naser, M.Z., 2023. "Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001138. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.