Abstract
The paper deals with the design of methodological backgrounds for the study of time-delayed piecewise-linear dynamical systems. These backgrounds allow us to define dynamical system responses to the internal and external disturbances in an analytical way. We define these responses as the piecewise functions of relative system operation time which is used by us to simplify the system model. According to the well-known method of solution of time-delayed differential equations, we split all operation time into equal slices, which are specified by the value of time delay. Due to operating with piecewise-linear right-hand expression in the differential equation, we split each slice into several stages if the output variable in the previous slice reaches the fracture point. Contrary to known methods of analytical solution of time-delayed differential equations the proposed one makes it possible to determine system motions as a function of time delay value and times where piecewise function is fractured. We formalize our approach and propose the algorithm to determine the considered system motions. We use this algorithm to study the Mackey-Glass equation with the constant parameters and piecewise-linear function in the right-hand expression. A comparison of the analytical and numerical solutions of this equation shows that the error does not exceed a step of numerical integration and gives us the possibility to claim the correctness of given formulas. Analysis of our formulas shows that the equation of the modified Mackey-Glass system can be solved analytically in advance and thus its motion can be predicted. This fact requires to use of chaotic systems with piecewise nonlinearity with big caution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mu, X., Yu, J., Wang, S.: The extended linear-drift model of memristor and its piecewise linear approximation. Tsinghua Sci. Technol. 19(3), 307–313 (2014). https://doi.org/10.1109/TST.2014.6838202
Satybaldiev, D.: A new method of piecewise linear approximation of non-stationary time series for similarity measurement. In: 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO), pp. 1–4. IEEE, Almaty (2015). https://doi.org/10.1109/ICECCO.2015.7416868
Zhai, J., Wu, S., Zhang, L., et al.: A 2-D-canonical piecewise linear function-based behavioral model for concurrent dual-band power amplifiers. IEEE Microwave Wirel. Compon. Lett. 28(11), 1050–1052 (2018). https://doi.org/10.1109/LMWC.2018.2873191
Hafsi, S., Laabidi, K., Ksouri-Lahmari, M.: Identification of Wiener-Hammerstein model with multisegment piecewise-linear characteristic. In: 2012 16th IEEE Mediterranean Electrotechnical Conference, pp. 5–10. IEEE, Yasmine Hammamet (2012). https://doi.org/10.1109/MELCON.2012.6196367
Bai, J., Zhai, Q., Zhou, Y.: A compact aggregated unit model for short-term hydro power generation scheduling based on optimal piecewise approximation. In: 2017 36th Chinese Control Conference (CCC), pp. 2882–2886. IEEE, Dalian (2017). https://doi.org/10.23919/ChiCC.2017.8027803
Volianskyi, R., Sadovoi, O., Volianska, N., Sinkevych, O.: Construction of parallel piecewise-linear interval models for nonlinear dynamical objects. In: 2019 9th International Conference on Advanced Computer Information Technologies (ACIT), pp. 97–100. IEEE, Ceske Budejovice (2019). https://doi.org/10.1109/ACITT.2019.8779945
Solomentsev, O., Zaliskyi, M., Shcherbyna, O., Kozhokhina, O.: Sequential procedure of changepoint analysis during operational data processing. In: 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW), vol. 1, pp. 168–171. IEEE, Riga (2020). https://doi.org/10.1109/MTTW51045.2020.9245068
Kuzmin, V.M., Zaliskyi, M.Y., Odarchenko, R.S., Petrova, Y.V.: New approach to switching points optimization for segmented regression during mathematical model building. CEUR Workshop Proc. 3077, 106–122 (2022)
Kuzenkov, O., et al.: Mathematical model of dynamics of homomorphic objects. CEUR Workshop Proc. 2516, 190–205 (2019)
Li, Y., Lin, Z.: Analysis of linear systems with piecewise linear functions in the input. In: 2016 35th Chinese Control Conference (CCC), pp. 5952–5957. IEEE, Chengdu (2016). https://doi.org/10.1109/ChiCC.2016.7554291
Meng, A., Lam, H.K., Liu, F., Yang, Y.: Filter design for positive T-S fuzzy continuous-time systems with time delay using piecewise-linear membership functions. IEEE Trans. Fuzzy Syst. 29(9), 2521–2531 (2021). https://doi.org/10.1109/TFUZZ.2020.3001744
Liu, X., Du, W.: Closed form solutions for the type reduction of general type-2 fuzzy sets with piecewise linear membership functions. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1232–1239. IEEE, Vancouver (2016). https://doi.org/10.1109/FUZZ-IEEE.2016.7737829
Doleel, P., Rozsíval, P., Marika, M.: Piecewise-linear neural network: A tool for modeling of the processes to be controlled. In: 2014 International Conference on Computational Science and Computational Intelligence, vol. 1, pp. 425–430. IEEE, Las Vegas (2014). https://doi.org/10.1109/CSCI.2014.76
Zagirnyak, M., Serhiienko, S., Serhiienko, I.: Improvement of the qualitative characteristics of an automatic control system with a fractional-order PID-controller. In: 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), pp. 1–4. IEEE, Kutna Hora (2017). https://doi.org/10.1109/CPEE.2017.8093062
Tryputen, M., et al.: One approach to quasi-optimal control of direct current motor. In: 2019 IEEE 5th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), pp. 190–193. IEEE, Kyiv (2019). https://doi.org/10.1109/APUAVD47061.2019.8943878
Tryputen, N., Kuznetsov, V., Kuznetsova, Y.: About the possibility of researching the optimal automatic control system on a physical model of a thermal object. In: 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 1244–1248). IEEE, Lviv (2019). https://doi.org/10.1109/UKRCON.2019.8879830
Zagirnyak, M., et al.: Refined calculation of induction motor equivalent circuit nonlinear parameters by an energy method. Eastern-Eur. J. Enterp. Technol. 3(5–87), 4–10 (2017). https://doi.org/10.15587/1729-4061.2017.104146
Wang, N., Li, C., Bao, H., Chen, M., Bao, B.: Generating multi-scroll Chua’s attractors via simplified piecewise-linear Chua’s diode. IEEE Tran. Circ. Syst. I: Regul. Pap. 66(12), 4767–4779 (2019). https://doi.org/10.1109/TCSI.2019.2933365
Dhivya, R., Premkumar, R., Nithyaa, A.N.: Real time secured transmission of biosignal using chaotic communication system. In: 2015 IEEE International Conference on Engineering and Technology (ICETECH), pp. 1–4. IEEE, Coimbatore (2015). https://doi.org/10.1109/ICETECH.2015.7275045
Amil, P., Cabeza, C., Marti, A.C.: Exact discrete-time implementation of the Mackey-Glass delayed model. IEEE Trans. Circuits Syst. II Expr. Briefs 62(7), 681–685 (2015). https://doi.org/10.1109/TCSII.2015.2415651
Hoang, T.M., Son, N.V., Nakagawa, M.: A secure communication system using projective-lag synchronization of multidelay Mackey-Glass systems. In: 2006 First International Conference on Communications and Electronics, pp. 325–330. IEEE, Hanoi (2006). https://doi.org/10.1109/CCE.2006.350801
Soto, J., Melin, P., Castillo, O.: Optimization of interval type-2 fuzzy integrators in ensembles of ANFIS models for prediction of the Mackey-Glass time series. In: 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), pp. 1–8. IEEE, Boston (2014). https://doi.org/10.1109/NORBERT.2014.6893880
Puthusserypady, S.K., Mahendra, C.: Mackey-Glass based communication scheme: systemf security analysis. In: 2004 Ninth International Conference on Communications Systems, pp. 321–325. IEEE, Singapore (2004). https://doi.org/10.1109/ICCS.2004.1359391
Farzad, M., Tahersima, H., Khaloozadeh, H.: Predicting the Mackey Glass chaotic time series using genetic algorithm. In: 2006 SICE-ICASE International Joint Conference, pp. 5460–5463. IEEE, Busan (2006). https://doi.org/10.1109/SICE.2006.315603
Ustundag, B.B., Kulaglic, A.: High-performance time series prediction with predictive error compensated wavelet neural networks. IEEE Access 8, 210532–210541 (2020). https://doi.org/10.1109/ACCESS.2020.3038724
Voliansky, R., et al.: Chaotic time-variant dynamical system. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 606–609. IEEE, Lviv-Slavske (2020). https://doi.org/10.1109/TCSET49122.2020.235503
Volianskyi, R., Sadovoi, O., Volianska, N., Sinkevych, O.: Root methods for dynamic analysis of the one class chaotic systems. In: 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 117–121. IEEE, Lviv (2019). https://doi.org/10.1109/STC-CSIT.2019.8929852
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Voliansky, R., Volianska, N., Sinkevych, O., Serhiienko, S., Kuznetsov, V. (2023). Analytical Solution of Modified Mackey-Glass Equation. In: Arsenyeva, O., Romanova, T., Sukhonos, M., Tsegelnyk, Y. (eds) Smart Technologies in Urban Engineering. STUE 2022. Lecture Notes in Networks and Systems, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-031-20141-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-20141-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20140-0
Online ISBN: 978-3-031-20141-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)