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Security Application Of Neural Networks Under The Inspection Of Nonlinear Dynamic Systems

Author

Listed:
  • XIAOBING CHEN

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China)

  • LIEHUANG ZHU

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China)

  • DANIYAL M. ALGHAZZAWI

    (��Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

  • ZHONGRU WANG

    (��Chinese Academy of Cyberspace Studies, Beijing, P. R. China)

  • QING GUO

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China)

Abstract

Based on the improved BP neural network, this paper establishes an adaptive online controlling model and adopts the model to optimize the controlling accuracies in discrete nonlinear dynamic systems and inverted pendulum systems. To avoid the local minimum problem of the BP neural network’s objective function in the training process, this paper proposes a neural network training method based on the quasi-Newton method (BFGS) optimization algorithm. Compared with other control methods, the neural network-based inverted pendulum control method proposed in this paper has higher control accuracy. Through the control simulation of its power system uses a discrete control method and the control of the inverted pendulum model system, this paper verifies the validity and good control has significantly improved the control method.

Suggested Citation

  • Xiaobing Chen & Liehuang Zhu & Daniyal M. Alghazzawi & Zhongru Wang & Qing Guo, 2022. "Security Application Of Neural Networks Under The Inspection Of Nonlinear Dynamic Systems," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(02), pages 1-9, March.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400618
    DOI: 10.1142/S0218348X22400618
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