Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jian-Lan Guo"'
Publikováno v:
IEEE Access, Vol 8, Pp 210988-211006 (2020)
With the popularization and development of the IoT(Internet of Things), more and more data needs to be transmitted over the Internet, which leads to the deterioration of network quality. The CDN (Content Distribution Network) technology is an importa
Externí odkaz:
https://doaj.org/article/d1345be0a14e4eddbe5aef9a07e6f694
Autor:
Jian-Lan Guo, Yu-Qiang Chen, Huai-De Yang, Chien-Ming Chen, Yeh-Cheng Chen, Huiyu Zhang, Zhiyu Zhang
Publikováno v:
IEEE Access, Vol 7, Pp 185580-185589 (2019)
The wireless sensor networks is a new technology for information acquisition and processing, which includes the techniques of sensor, computer, Internet of Things and network. Due to the fragility of wireless sensor networks, the security issue has b
Externí odkaz:
https://doaj.org/article/cb07a4b238984f4289db3b1b36e6cd4c
Publikováno v:
Journal of Information Science & Engineering; Jul2023, Vol. 39 Issue 4, p839-854, 16p
Neural networks-based adaptive control of uncertain nonlinear systems with unknown input constraints
Autor:
Yu-Qiang Chen, Hong-ling Liu, Jingjing Wang, Zhenhai Wang, Najla Al-Nabhan, Guan-yu Lai, Yuan Tian, Jian-lan Guo
Publikováno v:
Journal of Ambient Intelligence and Humanized Computing.
In this work, we solve the adaptive actuator backlash compensation control problem of uncertain nonlinear systems. A new generalized backlash model is first proposed, which takes both the actuator perturbation and unidentifiable coupling into account
Autor:
Jian-lan Guo, Yu-qiang Chen
Publikováno v:
Optik. 125:6407-6412
In this paper the issue of impulsive synchronization of a class of uncertain chaotic systems with parameters perturbation is investigated. Applying the impulsive theory and linear matrix inequality technique, some less conservative and easily verifie
Publikováno v:
Applied Mechanics and Materials. :2531-2536
FOCUSS(Focal Underdetennined System Solver) algorithm is a novel tool for sparse representation and underdetermined inverse problems. To improve the performance of FOCUSS in the presence of noise, we propose an improved FOCUSS algorithm using augment