SECURITY APPLICATION OF NEURAL NETWORKS UNDER THE INSPECTION OF NONLINEAR DYNAMIC SYSTEMS

Xiaobing Chen, Liehuang Zhu, Daniyal M. Alghazzawi (), Zhongru Wang and Qing Guo
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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

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-9

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.

Keywords: Neural network; Nonlinear dynamic system; Magnetic flux test; BP neural network (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22400618

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