Chengguang Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chengguang Zhang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jie Zhang
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Qifeng Niu
School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou 466001, China
1 shared publication - Qifeng Niu
School of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou 466001, China
1 shared publication - Xiaoguang Shao
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Yibo Zhao
School of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou 466001, China
1 shared publication
Latest Publications
- Open Access
- 24 January 2026
- Published by MDPI AG in Fractal and Fractional
- Vol. 10 (2) , 81
Event-Based State Estimator Design for Fractional-Order Memristive Neural Networks with Random Gain Fluctuations
This study addresses the issue of nonfragile state estimation for fractional-order memristive neural networks with time-varying delays under an adaptive event-triggered mechanism. Possible gain perturbations of the estimator are considered. A Bernoulli-distributed random variable is introduced to model the stochastic nature of gain fluctuations. The primary objective is to develop a nonfragile estimator that accurately estimates the network states. By means of Lyapunov functionals and fractional-order Lyapunov methods, two delay and order-dependent sufficient criteria are established to guarantee the mean-square stability of the augmented system. Finally, the effectiveness of the proposed estimation scheme is demonstrated through two simulation examples.
- 24 January 2026
- Published by MDPI AG in Fractal and Fractional
- Vol. 10 (2) , 81
Open AccessEvent-Based State Estimator Design for Fractional-Order Memristive Neural Networks with Random Gain Fluctuations
This study addresses the issue of nonfragile state estimation for fractional-order memristive neural networks with time-varying delays under an adaptive event-triggered mechanism. Possible gain perturbations of the estimator are considered. A Bernoulli-distributed random variable is introduced to model the stochastic nature of gain fluctuations. The primary objective is to develop a nonfragile estimator that accurately estimates the network states. By means of Lyapunov functionals and fractional-order Lyapunov methods, two delay and order-dependent sufficient criteria are established to guarantee the mean-square stability of the augmented system. Finally, the effectiveness of the proposed estimation scheme is demonstrated through two simulation examples.