标题:Extended H∞ Synchronization Control for Switched Neural Networks with Multi Quantization Densities Based on a Persistent Dwell-Time Approach
作者:Huang Z.; Shen H.; Xia J.; Huang X.; Wang J.
作者机构:[Huang, Z] School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, China;[ Shen, H] School of Electrical 更多
通讯作者:Huang, X(huangxia_qd@126.com)
通讯作者地址:[Huang, X] College of Electrical Engineering and Automation, Shandong University of Science and TechnologyChina;
来源:Neural Processing Letters
出版年:2019
DOI:10.1007/s11063-019-10064-2
关键词:Multi quantization densities; Persistent dwell-time; Switched neural networks; Synchronization control
摘要:This paper thoroughly investigates the synchronization control issue for the switched neural networks. The more comprehensive comparatively switching rule, persistent dwell-time, is applied to actuate the aforementioned neural networks. For tackling the problem caused by the transmission of tremendous data, the quantizer is utilized. The objective is to establish the mixed controller with multi quantization densities for the synchronization error neural networks to meet the various accuracy requirements of the transmitted data. Whereafter, the sufficient conditions of the extended H∞ performance and global uniform exponential stability for the synchronization error neural networks are constructed. Conclusively, the capability of the proposed mixed controller is elucidated through a numerical example. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068108882&doi=10.1007%2fs11063-019-10064-2&partnerID=40&md5=eb1dc532510609dc0265da49e6650963
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