Open AccessSECURITY APPLICATION OF NEURAL NETWORKS UNDER THE INSPECTION OF NONLINEAR DYNAMIC SYSTEMS
Author(s) -
Xiaobing Chen,
Liehuang Zhu,
Daniyal Alghazzawi,
Wang Zhong-ru,
Qing Guo
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400618
Subject(s) - inverted pendulum , broyden–fletcher–goldfarb–shanno algorithm , artificial neural network , control theory (sociology) , computer science , nonlinear system , process (computing) , double inverted pendulum , control (management) , artificial intelligence , physics , quantum mechanics , computer network , asynchronous communication , operating system
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.