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On the development of an intelligent controller for neural networks: a type 2 fuzzy and chatter-free approach for variable-order fractional cases

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Abstract

The rise of artificial intelligence has revolutionized all aspects of today's life. Neural networks, which are a stepping stone in the search for artificial intelligence, considerably affect the pace of advances in this field. Therefore, developing trustable tools for their modeling and control is of crucial importance. Motivated by this, we propose an intelligent controller for neural networks. Although the proposed approach is based on the sliding mode concept, it is chatter-free and provides smooth results. A type 2 fuzzy observer is applied to estimate the unknown function of the chaotic neural networks and enhance the performance of the proposed controller. This way, the proposed controller will act smartly in unseen conditions. The offered disturbance observer possesses an updating mechanism that modifies the type 2 fuzzy observer’s weights. Using the Lyapunov stability method, the analysis of stability is performed, and it is guaranteed that the proposed control scheme is asymptotically stable. Finally, the simulation results are presented to show the effectiveness of the offered method for the chaotic variable-order fractional neural networks under uncertainties and unknown external disturbances.

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References

  1. J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554–2558 (1982)

    Article  ADS  MathSciNet  Google Scholar 

  2. L.O. Chua, L. Yang, Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35(10), 1273–1290 (1988). https://doi.org/10.1109/31.7601

    Article  MathSciNet  Google Scholar 

  3. M.A. Cohen, S. Grossberg, Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans. Syst. Man Cybern. SMC-13(5), 815–826 (1983). https://doi.org/10.1109/TSMC.1983.6313075

    Article  MathSciNet  MATH  Google Scholar 

  4. H. Jahanshahi et al., Simulation and experimental validation of a non-equilibrium chaotic system. Chaos Solitons Fractals 143, 110539 (2021)

    Article  MathSciNet  Google Scholar 

  5. H. Jahanshahi et al., On the dynamical investigation and synchronization of variable-order fractional neural networks: the Hopfield-like neural network model. Eur. Phys. J. Spec. Top (2022). https://doi.org/10.1140/epjs/s11734-022-00450-8

    Article  Google Scholar 

  6. B. Wang et al., Tracking control and stabilization of a fractional financial risk system using novel active finite-time fault-tolerant controls. Fractals 29(6), 2150155–2150177 (2021)

    Article  ADS  Google Scholar 

  7. H. Jahanshahi, A. Yousefpour, Z. Wei, R. Alcaraz, S. Bekiros, A financial hyperchaotic system with coexisting attractors: dynamic investigation, entropy analysis, control and synchronization. Chaos Solitons Fractals 126, 66–77 (2019)

    Article  ADS  MathSciNet  Google Scholar 

  8. S. Soradi-Zeid, H. Jahanshahi, A. Yousefpour, S. Bekiros, King algorithm: a novel optimization approach based on variable-order fractional calculus with application in chaotic financial systems. Chaos Solitons Fractals 132, 109569 (2020)

    Article  MathSciNet  Google Scholar 

  9. B. Wang et al., Incorporating fast and intelligent control technique into ecology: a Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems. Ecol. Complex. 47, 100943 (2021)

    Article  Google Scholar 

  10. H. Wang, X.-J. Zhu, S.-W. Gao, Z.-Y. Chen, Singular observer approach for chaotic synchronization and private communication. Commun. Nonlinear Sci. Numer. Simul. 16(3), 1517–1523 (2011). https://doi.org/10.1016/j.cnsns.2010.06.021

    Article  ADS  Google Scholar 

  11. S. Vaidyanathan, Adaptive synchronization of novel 3-D chemical chaotic reactor systems. Parameters 1, 4 (2015)

    Google Scholar 

  12. H.D.I. Abarbanel, R. Brown, J.J. Sidorowich, LSh. Tsimring, The analysis of observed chaotic data in physical systems. Rev. Mod. Phys. 65(4), 1331–1392 (1993). https://doi.org/10.1103/RevModPhys.65.1331

    Article  ADS  MathSciNet  Google Scholar 

  13. V. Sundarapandian, I. Pehlivan, Analysis, control, synchronization, and circuit design of a novel chaotic system. Math. Comput. Model. 55(7), 1904–1915 (2012). https://doi.org/10.1016/j.mcm.2011.11.048

    Article  MathSciNet  MATH  Google Scholar 

  14. A. El-Gohary, Chaos and optimal control of cancer self-remission and tumor system steady states. Chaos Solitons Fractals 37(5), 1305–1316 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  15. C.R. Mirasso, P. Colet, P. Garcia-Fernandez, Synchronization of chaotic semiconductor lasers: application to encoded communications. IEEE Photonics Technol. Lett. 8(2), 299–301 (1996). https://doi.org/10.1109/68.484273

    Article  ADS  Google Scholar 

  16. H. Lin, C. Wang, Q. Deng, C. Xu, Z. Deng, C. Zhou, Review on chaotic dynamics of memristive neuron and neural network. Nonlinear Dyn. 106(1), 959–973 (2021)

    Article  Google Scholar 

  17. G. Maddodi, A. Awad, D. Awad, M. Awad, B. Lee, A new image encryption algorithm based on heterogeneous chaotic neural network generator and dna encoding. Multimed. Tools Appl. 77(19), 24701–24725 (2018)

    Article  Google Scholar 

  18. K. Aihara, T. Takabe, M. Toyoda, Chaotic neural networks. Phys. Lett. A 144(6–7), 333–340 (1990)

    Article  ADS  MathSciNet  Google Scholar 

  19. L. Cui, C. Chen, J. Jin, F. Yu, Dynamic analysis and FPGA implementation of new chaotic neural network and optimization of traveling salesman problem. Complexity 2021, 5521192 (2021)

    Google Scholar 

  20. C.-J. Cheng, C.-B. Cheng, An asymmetric image cryptosystem based on the adaptive synchronization of an uncertain unified chaotic system and a cellular neural network. Commun. Nonlinear Sci. Numer. Simul. 18(10), 2825–2837 (2013). https://doi.org/10.1016/j.cnsns.2013.02.011

    Article  ADS  MathSciNet  MATH  Google Scholar 

  21. D. Rickles, P. Hawe, A. Shiell, A simple guide to chaos and complexity. J. Epidemiol. Community Health 61(11), 933–937 (2007). https://doi.org/10.1136/jech.2006.054254

    Article  Google Scholar 

  22. J. Kurths, S. Boccaletti, C. Grebogi, Y.-C. Lai, Introduction: control and synchronization in chaotic dynamical systems. Chaos Interdiscip. J. Nonlinear Sci. 13(1), 126–127 (2003). https://doi.org/10.1063/1.1554606

    Article  Google Scholar 

  23. J. Xiao, S. Zhong, S. Wen, Improved approach to the problem of the global Mittag–Leffler synchronization for fractional-order multidimension-valued BAM neural networks based on new inequalities. Neural Netw. 133, 87–100 (2021)

    Article  Google Scholar 

  24. J. Xiao, Y. Li, S. Wen, Mittag–Leffler synchronization and stability analysis for neural networks in the fractional-order multi-dimension field. Knowl. Based Syst. 231, 107404 (2021)

    Article  ADS  Google Scholar 

  25. Y. Liu, R. Tang, C. Zhou, Z. Xiang, X. Yang, Event-triggered leader-following consensus of multiple mechanical systems with switched dynamics. Int. J. Syst. Sci. 51(16), 3563–3572 (2020)

    Article  ADS  MathSciNet  Google Scholar 

  26. J. Xiao, J. Cheng, K. Shi, R. Zhang, A general approach to fixed-time synchronization problem for fractional-order multi-dimension-valued fuzzy neural networks based on memristor. IEEE Trans. Fuzzy Syst. 30, 968–977 (2021)

    Article  Google Scholar 

  27. A. Sharifi, A. Sharafian, Q. Ai, Adaptive MLP neural network controller for consensus tracking of multi-agent systems with application to synchronous generators. Expert Syst. Appl. 184, 115460 (2021)

    Article  Google Scholar 

  28. J. Xiao, J. Cao, J. Cheng, S. Wen, R. Zhang, S. Zhong, Novel inequalities to global Mittag–Leffler synchronization and stability analysis of fractional-order quaternion-valued neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3700–3709 (2020)

    Article  MathSciNet  Google Scholar 

  29. A. Sharafian, V. Bagheri, W. Zhang, RBF neural network sliding mode consensus of multiagent systems with unknown dynamical model of leader-follower agents. Int. J. Control Autom. Syst. 16(2), 749–758 (2018)

    Article  Google Scholar 

  30. A. Sharafian, A. Sharifi, W. Zhang, Different types of sliding mode controller for nonlinear fractional multi-agent system. Chaos Solitons Fractals 131, 109481 (2020)

    Article  MathSciNet  Google Scholar 

  31. H. Yatimi, E. Aroudam, Assessment and control of a photovoltaic energy storage system based on the robust sliding mode MPPT controller. Sol. Energy 139, 557–568 (2016). https://doi.org/10.1016/j.solener.2016.10.038

    Article  ADS  Google Scholar 

  32. S. Mobayen, An LMI-based robust controller design using global nonlinear sliding surfaces and application to chaotic systems. Nonlinear Dyn. 79(2), 1075–1084 (2015)

    Article  MathSciNet  Google Scholar 

  33. A. Yousefpour, H. Jahanshahi, Fast disturbance-observer-based robust integral terminal sliding mode control of a hyperchaotic memristor oscillator. Eur. Phys. J. Spec. Top. 228(10), 2247–2268 (2019)

    Article  Google Scholar 

  34. A. Yousefpour, A.H. Hosseinloo, M.R.H. Yazdi, A. Bahrami, Disturbance observer-based terminal sliding mode control for effective performance of a nonlinear vibration energy harvester. J. Intell. Mater. Syst. Struct. 31(12), 1495–1510 (2020). https://doi.org/10.1177/1045389X20922903

    Article  Google Scholar 

  35. S. Wang et al., Synchronization of a non-equilibrium four-dimensional chaotic system using a disturbance-observer-based adaptive terminal sliding mode control method. Entropy 22(3), 271 (2020)

    Article  ADS  MathSciNet  Google Scholar 

  36. M. Chen, W. Chen, Sliding mode control for a class of uncertain nonlinear system based on disturbance observer. Int. J. Adapt. Control Signal Process. 24(1), 51–64 (2010)

    MathSciNet  MATH  Google Scholar 

  37. A. Sharafian, R. Ghasemi, Fractional neural observer design for a class of nonlinear fractional chaotic systems. Neural Comput. Appl. 31(4), 1201–1213 (2019). https://doi.org/10.1007/s00521-017-3153-y

    Article  Google Scholar 

  38. A. Mohammadzadeh, S. Ghaemi, O. Kaynak, S. Khanmohammadi, Observer-based method for synchronization of uncertain fractional order chaotic systems by the use of a general type-2 fuzzy system. Appl. Soft Comput. 49, 544–560 (2016). https://doi.org/10.1016/j.asoc.2016.08.016

    Article  Google Scholar 

  39. S.-S. Zhou et al., Discrete-time macroeconomic system: bifurcation analysis and synchronization using fuzzy-based activation feedback control. Chaos Solitons Fractals 142, 110378 (2021)

    Article  MathSciNet  Google Scholar 

  40. Y.-L. Wang, H. Jahanshahi, S. Bekiros, F. Bezzina, Y.-M. Chu, A.A. Aly, Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence. Chaos Solitons Fractals 146, 110881 (2021)

    Article  MathSciNet  Google Scholar 

  41. S. Bekiros, H. Jahanshahi, F. Bezzina, A.A. Aly, A novel fuzzy mixed H2/H∞ optimal controller for hyperchaotic financial systems. Chaos Solitons Fractals 146, 110878 (2021)

    Article  Google Scholar 

  42. Z. Liu et al., Fuzzy adaptive control technique for a new fractional-order supply chain system. Phys. Scr. 96(12), 124017 (2021)

    Article  ADS  Google Scholar 

  43. H.-B. Bao, J.-D. Cao, Projective synchronization of fractional-order memristor-based neural networks. Neural Netw. 63, 1–9 (2015)

    Article  Google Scholar 

  44. F. Jarad, T. Abdeljawad, D. Baleanu, Stability of q-fractional non-autonomous systems. Nonlinear Anal. Real World Appl. 14(1), 780–784 (2013)

    Article  MathSciNet  Google Scholar 

  45. C.T. Leondes, Fuzzy theory systems (Academic Press, Cambridge, 1999)

    MATH  Google Scholar 

  46. H.A. Hagras, A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004)

    Article  Google Scholar 

  47. Y. Li, Y. Chen, I. Podlubny, Stability of fractional-order nonlinear dynamic systems: Lyapunov direct method and generalized Mittag–Leffler stability. Comput. Math. Appl. 59(5), 1810–1821 (2010). https://doi.org/10.1016/j.camwa.2009.08.019

    Article  MathSciNet  MATH  Google Scholar 

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Yousefpour, A., Yasami, A., Beigi, A. et al. On the development of an intelligent controller for neural networks: a type 2 fuzzy and chatter-free approach for variable-order fractional cases. Eur. Phys. J. Spec. Top. 231, 2045–2057 (2022). https://doi.org/10.1140/epjs/s11734-022-00612-8

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