Abstract
As a result of the research, a combined neural network method, models and modeling tools for the operational control of complex technical systems have been developed and investigated. The analysis of existing methods, models and software for operational management of complex technical systems; the requirements for the developed methods, models and software for managing these systems have been substantiated. A combined neural network method for modeling complex technical systems based on a hierarchical neural network model is proposed, combining the capabilities of analytical and neural network approaches and allowing to significantly increase the efficiency of operational management (quality and efficiency of management decisions), as well as the flexibility of modeling and the possibility of operational management of calculation of structural and parametric optimization of the model based on information about the system obtained in real time. A hierarchical neural network model for the operational control of complex technical systems has been developed, consisting at the lower level of a set of interconnected neural network models corresponding to the elements of the modeled system, and a supervisor at the upper level designed to identify structural errors at the lower level of the model. A method of decision support for managing complex technical systems in real and pseudo-real time, based on the proposed hierarchical neural network model, is proposed. Software tools for combined neural network modeling and decision support have been developed for the operational control of complex technical systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Law AM, McComas MG (2001) How to build valid and credible simulation models. In: Proceedings of the 2001 winter simulation conference, pp 22–29
Perakath B (2000) A model-based approach for component simulation development. In: Proceedings of the 2000 winter simulation conference, pp 1831–1839
Kryzhanovsky BV, Litinskii LB, Mikaelian AL (2004) Vector-neuron models of associative memory. In: Proceedings of international joint conference on neural networks, Budapest, pp 909–1004
Carson JS (2002) Model verification and validation. In: Proceedings of the 2002 winter simulation conference, pp 52–58
MacGarry K, Wermter S (1999) Hybrid neural system: from simply coupling to fully integrated neural network. Neural Coupling Surv 2:62–93
Majors M, Stori J, Cho DI (1994) Neural network control of automatic systems. IEEE Control Syst 14(3):31–36
Pavlovsky YuN, Belotelov NV, Brodsky YuI (2008) Simulation modeling. M.: Academy
Moser JG (1992) Integration of artificial intelligence and simulation in comprehensive decision - support systems. Simulation 47:6
Fujimoto RM (2003) Distributed simulation systems. In: Chick S, Sánchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of the 2003 winter simulation conference, pp 124–134
Girossi F, Jones M, Poggio T (1995) Regularization theory and neural networks architectures. Neural Comput 7:219–269
Beck MB et al (1993) Construction and evaluation of models of environment system. Modeling Change in Environmental System, Chichester
Bagrodia R, Meyer R et al (1998) Parsec: a parallel simulation environment for complex systems. IEEE Comput 31(10):77–85
Balci O (1986) Credibility assessment of simulation results. In: Proceedings of the 1986 winter simulation conference, pp 39–44
Diamantras KI, Kung SY (1996) Principal component neural networks. Wiley, New York
Fogel D, Fogel L, Porto V (1990) Evolving neural networks. Biol Cybern 63:487–493
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kozhevnikova, T., Manzhula, I. (2022). Simulation Modeling Using Neural Networks to Control Complex Technical Systems. In: Mottaeva, A. (eds) Technological Advancements in Construction. Lecture Notes in Civil Engineering, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-030-83917-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-83917-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-83916-1
Online ISBN: 978-3-030-83917-8
eBook Packages: EngineeringEngineering (R0)