THE AUTOMATIC SYNTHESIS OF PETRI NETS BASED ON THE FUNCTIONING OF ARTIFICIAL NEURAL NETWORK

Authors

  • A. A. Gurskiy Odessa National Academy of Food Technologies, Odessa, Ukraine. , Ukraine
  • A. V. Denisenko Odessa National Polytechnic University, Odessa, Ukraine. , Ukraine
  • S. M. Dubna Odessa National Academy of Food Technologies, Odessa, Ukraine. , Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2021-2-9

Keywords:

Petri net, artificial neural networks, coordinating automatic control system, algorithms of tuning, automatic synthesis.

Abstract

Context. The important task was solved during the scientific research related to the development of the methods for automatic synthesis of Petri nets while tuning up of the coordinating automatic control systems. The importance of development of these methods is due to the evolution of intelligent systems. These systems provide the automation of labor intensive processes in the particular case this is the tuning of the certain type of complex control systems.

Objective. The purpose of the scientific work is to minimize the time and automation of process in tuning of the multilevel coordinating automatic control systems.

Method. The principle of automatic synthesis of Petri nets and the implementation of certain algorithms for tuning complex control systems based on the functioning of an artificial neural network are proposed. The mathematical description of the method for changing the coefficients in neural connections of network in the synthesis of Petri nets is presented.

Results. The experiments were conducted in the Matlab\Simulink 2012a environment. These experiments were bound to the joint functioning of an artificial neural network and Petri nets. The functioning of Petri nets was presented in the Matlab \ Simulink environment using Statflow diagrams.

As a result of the experiments we have obtained the temporal characteristics of the functioning of artificial neural network providing the composition of Petri nets. The fundamental suitability of using artificial neural network to provide the automatic composition of Petri nets was determined on the basis of analysis of temporal characteristics.

Conclusion. The problem linked to the development of system for the joint functioning of neural network and Petri nets for the formation of algorithms and sequential calculations was solved in this work. Thus the method of automatic synthesis of Petri nets and the method of developing of the certain algorithms based on the functioning of a neural network were further developed.

Author Biographies

A. A. Gurskiy , Odessa National Academy of Food Technologies, Odessa, Ukraine.

PhD, Associate Professor of the Department of Technological Processes Automation and Robot-technical Systems.

A. V. Denisenko , Odessa National Polytechnic University, Odessa, Ukraine.

Lecturer of the Department of Information Systems.

S. M. Dubna , Odessa National Academy of Food Technologies, Odessa, Ukraine.

Lecturer of the Department of Technological Processes Automation and Robot-technical Systems.

References

Yang Z., Blanke M. A unified approach to controllability analysis for hybrid control systems, Nonlinear Analysis: Hybrid Systems, 2007, Volume 1, Issue 2, pp. 212–222. DOI: 10.1016/j.nahs.2006.08.002

Drighiciu, M. A. Hybrid Petri nets: A framework for hybrid systems modeling, 2017 International Conference on Electromechanical and Power Systems (SIELMEN), IEEE, 2017, pp. 020–025. DOI: 10.1109 /SIELMEN. 2017. 8123285

Filimonov A. B., Filimonov N. B. O problematike sinteza koordiniruyuschih sistem avtomaticheskogo upravleniya, Izvestiya SfedU, Engineering sciences, 2012, Vol. 3, pp. 172– 180. ISSN 1999–9429 2311–3103

Peterson, J. L. Petri net theory and the modeling of systems / Prentice Hall PTR, 1981, 290 p. ISBN 0–13–661983–5

He D. W., Strege B., Tolle H., Kusiak A. Decomposition in automatic generation of Petri nets for manufacturing system control and scheduling, International Journal of Production Research, 2000, Volume 38, Issue 6, pp. 1437–1457. DOI: 10.1080/002075400188942

Ndiaye M. A., Petin J. F., Camerini J., Georges J. P. Performance assessment of industrial control system during pre-sales uncertain context using automatic Colored Petri Nets model generation, 2016 International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, 2016, pp. 671–676. DOI: 10.1109/CoDIT.2016.7593643

Durmuş M. S., Yıldırım U., Söylemez M. T. Automatic generation of Petri Net supervisors for railway interlocking design, 2012 2nd Australian Control Conference, IEEE, 2012, pp. 180–185. ISBN: 978-1-922107-63-3

Gurskiy A. A., Dubna S. M. Nastroıka neıronnoı setı prı avtomatıcheskom sınteze seteı Petrı, Automation of technological and business processes, 2018, No. 1, pp. 22–32. DOI: https://doi.org/10.15673/ atbp.v10i1.877

Ahson, S. I. Petri net models of fuzzy neural networks, IEEE Transactions on systems, man, and cybernetics, 1995, Volume 38, Issue 6, pp. 926–932. ISSN: 2168-2909

Chow T. W., Li J. Y. Higher-order Petri net models based on artificial neural networks, Artificial Intelligence, 1997, Volume 92, Issues 1–2, pp. 289–300. DOI: https://doi.org/10.1016/S0004-3702(96)00048-3

Hanna M. M., Buck A., Smith R. Fuzzy Petri nets with neural networks to model products quality from a CNC-milling machining centre, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 1996, Vol. 26, Issue 5, pp. 638–645. ISSN: 1558-2426. DOI: 10.1109/3468.531910

Boichuk L. M. Sintez koordinirujushhih sistem avtomaticheskogo upravlenija. Moscow, Energoatomizdat, 1991, 160 p. ISBN 5–283– 01521–1

Gurskiy A. A., Goncharenko A. E., Dubna S. M. Algorithms for tuning of the coordinating automatic control systems [Теxt], Radio electronics, computer science, control, 2020, No. 1, pp. 190–199. DOI: https://doi.org/10.15588/16073274-2020-1-19

Sutton, R. S., Barto A. G. Reinforcement learning: An introduction, MIT press, 2018, 322 p. ISBN 0–262–19398–1

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Published

2021-07-03

How to Cite

Gurskiy , A. A., Denisenko , A. V., & Dubna , S. M. (2021). THE AUTOMATIC SYNTHESIS OF PETRI NETS BASED ON THE FUNCTIONING OF ARTIFICIAL NEURAL NETWORK . Radio Electronics, Computer Science, Control, (2), 84–92. https://doi.org/10.15588/1607-3274-2021-2-9

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Section

Neuroinformatics and intelligent systems