Application of Wide Learning System in Genesio System Synchronization
Corressponding author's email:
duchung.pham@utehy.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1716Keywords:
Genesio System, Wide Learning System, Neural network, Nonlinear system, Lyapunov's theoremAbstract
In this paper, the authors will introduce the application of a deep learning system to synchronize the Genesio chaotic system. The Genesio system is a nonlinear system with high complexity, and to synchronize the system, it is necessary to combine different formulas and algorithms, along with neural networks, to ensure the goal of optimizing inputs, minimizing system errors at the output, and achieving the desired signals. The synchronization scheme uses Lyapunov's theorem to maintain the system's stability, which ensures that the simulation results achieve higher accuracy. A simulation of the synchronized system is carried out using software with an application for synchronizing the Genesio system. The simulation results demonstrate accuracy and faster synchronization capability when compared to other traditional methods. Therefore, this research confirms that the use of a deep learning system combined with neural network techniques and stability theory can provide a powerful and optimal solution for controlling and synchronizing the Genesio system.
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References
J. H. Park, "Synchronization of Genesio chaotic system via backstepping approach," Chaos, Solitons & Fractals, vol. 27, no. 5, pp. 1369–1375, 2006.
C. M. Lin, Y. F. Peng, and M. H. Lin, "CMAC-based adaptive backstepping synchronization of uncertain chaotic systems," Chaos, Solitons & Fractals, vol. 42, no. 2, pp. 981–988, 2009.
C. M. Lin, D. H. Pham, and T. T. Huynh, "Synchronization of chaotic system using a brain-imitated neural network controller and its applications for secure communications," IEEE Access, vol. 9, pp. 75923–75944, 2021.
C. M. Lin, D. H. Pham, and T. T. Huynh, "Encryption and decryption of audio signal and image secure communications using chaotic system synchronization control by TSK fuzzy brain emotional learning controllers," IEEE Trans. Cybern., vol. 52, no. 12, pp. 13684–13698, Dec. 2022.
D. H. Pham, C. M. Lin, V. N. Giap, T. T. Huynh, and H. Y. Cho, "Wavelet interval type-2 Takagi-Kang-Sugeno hybrid controller for time-series prediction and chaotic synchronization," IEEE Access, vol. 10, pp. 104313–104327, 2022.
T. T. Huynh, C. M. Lin, and D. H. Pham, “Memristive Chaotic Systems-Based Audio Secure Communication Using Dual-Function-Link Fuzzy Brain Emotional Controller,” Int. J. Fuzzy Syst., vol 24, pp. 2946–2968, 2022. https://doi.org/10.1007/s40815-022-01312-0.
C. L. P. Chen and Z. Liu, "Broad learning system: An effective and efficient incremental learning system without the need for deep architecture," IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 1, pp. 10–24, Jan. 2018.
S. Feng and C. L. P. Chen, "Broad learning system for control of nonlinear dynamic systems," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2018.
L. Zhang et al., "Analysis and variants of broad learning system," IEEE Trans. Syst., Man, Cybern.: Syst., vol. 52, no. 1, pp. 334–344, 2020.
T. Li, S. Tong, Y. Xiao, and Q. Shan, "Broad learning system approximation-based adaptive optimal control for unknown discrete-time nonlinear systems," IEEE Trans. Syst., Man, Cybern.: Syst., vol. 52, no. 8, pp. 5028–5038, 2021.
S. Sui, C. P. Chen, S. Tong, and S. Feng, "Finite-time adaptive quantized control of stochastic nonlinear systems with input quantization: A broad learning system based identification method," IEEE Trans. Ind. Electron., vol. 67, no. 10, pp. 8555–8565, 2019.
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