title: Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks.
abstract: This paper shows that the globally exponentially stable neural network with time-varying delay and bounded noises may converge faster than those without noise. And the influence of noise on global exponential stability of DNNs was analyzed quantitatively. By comparing the upper bounds of noise intensity with coefficients of global exponential stability, we could deduce that noise is able to further express exponential decay for DNNs. The upper bounds of noise intensity are characterized by solving transcendental equations containing adjustable parameters. In addition, a numerical example is provided to illustrate the theoretical result.
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: College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: yangqiqikiki@163.com.
authors.forename: Qiqi
authors.initials: Q
authors.lastname: Yang
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: D016571:Neural Networks, Computer; D059629:Signal-To-Noise Ratio; D013997:Time Factors
keywords: Adjustable parameter; Delayed neural networks; Exponential decay; Noise