Management methods of information systems based on the regularizing Bayesian approach

L.S. Zvyagin

doi:10.18127/j19997493-202605-02

Management methods of information systems based on the regularizing Bayesian approach

research article UDC 004.9 Pages: 9-18 doi.org/10.18127/j19997493-202605-02

¹ Financial University under the Government of the Russian Federation (Moscow, Russia)

Keywords: Information systems regularizing Bayesian approach operational management uncertainty simulation system dynamics integral risk automated management systems

Abstract

The article discusses the urgent problem of increasing the sustainability and efficiency of management in complex information systems (IS) in the context of environmental uncertainty. The problem of the study lies in the insufficient stability of traditional stochastic parameters, which turn out to be ineffective in the presence of noisy measurements and inaccurate information.

A comprehensive management model based on the regularizing Bayesian approach (RBP) is proposed and mathematically described. The methods of system analysis, optimal control theory, mathematical statistics and simulation modeling were used in the article.

The dynamic characteristics of automated control systems have been studied and integral risks have been predicted using Bayesian regression. The results obtained confirm that the use of RBP can reduce the integral management risk by 78.1% and improve the accuracy of achieving targets by 22.5%. An original interpretation of the regularization parameter is presented as a tool for adapting the system to structural constraints and interference in the information flow.

Received: 02.09.2026
Approved after review: 08.09.2026
Accepted: 01.10.2026

How to cite

Звягин Л.С. Методы управления информационными системами на основе регуляризирующего байесовского подхода // Динамика сложных систем - XXI век. 2026. Т. 20. № 5. С. 9−18. DOI: https://doi.org/10.18127/j19997493-202605-02

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[1] Awan S.E., Raja M.A.Z., Awais M., Shu C.M. Intelligent Bayesian regularization networks for bio-convective nanofluid flow model inVolving gyro-tactic organisms with viscous dissipation, stratification and heat immersion. Engineering Applications of Computational Fluid Mechanics. 2021. V. 15. № 1. P. 1508–1530.

[2] Boyeau P., Regier J., Gayoso A., Jordan M.I., Lopez R., Yosef N. An empirical Bayes method for differential expression analysis of single cells with deep generative models. Proceedings of the National Academy of Sciences. 2023. V. 120. № 21. P. e2209124120.

[3] Cheng L., Yin F., Theodoridis S., Chatzis S., Chang T.H. Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling. IEEE Signal Processing Magazine. 2022. V. 39. № 6. P. 18–52.

[4] Garcia S., Mora-Merchan J.M., Larios D.F., Personal E., Parejo A., Leon C. Phase topology identification in low-Voltage distribution networks: A Bayesian approach. International Journal of Electrical Power & Energy Systems. 2023. V. 144. P. 108525.

[5] Hon C.K., Sun C., Xia B., Jimmieson N.L., Way K.A., Wu P.P.Y. Applications of Bayesian approaches in construction management research: a systematic review. Engineering, Construction and Architectural Management. 2022. V. 29. № 5. P. 2153–2182.

[6] Karkaria V., Goeckner A., Zha R., Chen J., Zhang J., Zhu Q., Chen W. Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization. Journal of Manufacturing Systems. 2024. V. 75. P. 322–332.

[7] Kiani A.K., Khan W.U., Raja M.A.Z., He Y., Sabir Z., Shoaib M. Intelligent backpropagation networks with bayesian regularization for mathematical models of environmental economic systems. Sustainability. 2021. V. 13. № 17. P. 9537.

[8] Kong Y., Zhang X., Mahadevan S. Bayesian deep learning for aircraft hard landing safety assessment. IEEE Transactions on Intelligent Transportation Systems. 2022. V. 23. № 10. P. 17062–17076.

[9] Kumar S.N.P. Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology. 2022. V. 4. № 01-03. P. 67–79.

[10] Liu R.L., Hua Y., Zhou Z.F., Li Y., Wu W.T., Aubry N. Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method. Physics of Fluids. 2022. V. 34. № 11. P. 114104.

[11] Mohammad-Djafari A. Regularization, Bayesian inference, and machine learning methods for inverse problems. Entropy. 2021. V. 23. № 12. P. 1673.

[12] Shi X., Kang Q., An J., Zhou M. Novel L1 regularized extreme learning machine for soft-sensing of an industrial process. IEEE Transactions on Industrial Informatics. 2021. V. 18. № 2. P. 1009–1017.

[13] Sindhu T.N., Colak A.B., Lone S.A., Shafiq A. Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization. Quality and Reliability Engineering International. 2023. V. 39. № 6. P. 2398–2421.

[14] Wu D., Huang H., Qiu S., Liu Y., Wu Y., Ren Y., Mou J. Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state. Energy Reports. 2022. V. 8. P. 3041–3050.

[15] Zeng L., Ge Z. Bayesian network for dynamic variable structure learning and transfer modeling of probabilistic soft sensor. Journal of Process Control. 2021. V. 100. P. 20–29.

Abstract

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