title: Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances.
abstract: In this paper, further results on robustness analysis of global exponential stability of recurrent neural networks (RNNs) subjected to time delays and random disturbances are provided. Novel exponential stability criteria for the RNNs are derived, and upper bounds of the time delay and noise intensity are characterized by solving transcendental equations containing adjustable parameters. Through the selection of the adjustable parameters, the upper bounds are improved. It shows that our results generalize and improve the corresponding results of recent works. In addition, some numerical examples are given to show the effectiveness of the results we obtained.
authors.affiliation: College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: luo425wei@163.com.
authors.forename: Weiwei
authors.initials: W
authors.lastname: Luo
authors.affiliation: College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Zhong
authors.affiliation: College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: songzhu82@gmail.com.
authors.forename: Song
authors.initials: S
authors.lastname: Zhu
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: yishen64@163.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Shen
mesh_terms: D003198:Computer Simulation; D016571:Neural Networks, Computer; D011336:Probability; D015203:Reproducibility of Results; D013997:Time Factors
keywords: Adjustable parameters; Global exponential stability; Random disturbances; Recurrent neural networks; Time delays