Your browser doesn't support javascript.
loading
Predefined-time synchronization of competitive neural networks.
Chen, Chuan; Mi, Ling; Liu, Zhongqiang; Qiu, Baolin; Zhao, Hui; Xu, Lijuan.
Affiliation
  • Chen C; School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China; State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications), Beijing 100876, China; Shandong Provincial Key Laboratory of Computer Network
  • Mi L; School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China. Electronic address: miling@lyu.edu.cn.
  • Liu Z; School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454003, China. Electronic address: zhongqiang@hpu.edu.cn.
  • Qiu B; School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China. Electronic address: qiubaolin@jxufe.edu.cn.
  • Zhao H; Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China. Electronic address: ise_zhaohui@ujn.edu.cn.
  • Xu L; Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center(National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China. Electronic address: xulj@sdas.org.
Neural Netw ; 142: 492-499, 2021 Oct.
Article in En | MEDLINE | ID: mdl-34280692
ABSTRACT
In this paper, the predefined-time synchronization of competitive neural networks (CNNs) is researched based on two different predefined-time stability theorems. In view of the bilayer structure of CNNs, we design two bilayer predefined-time controllers. The first controller utilizes sign function, while the second controller utilizes exponential function and Lyapunov function. In these two controllers, the predefined time is set as a controller parameter, and it can be an arbitrary positive constant. Under these two controllers, the considered CNNs can achieve synchronization within the predefined time regardless of the initial values. A specific example is presented to validate the theoretical results.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Neural Networks, Computer Type of study: Prognostic_studies Language: En Journal: Neural Netw Journal subject: NEUROLOGIA Year: 2021 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Neural Networks, Computer Type of study: Prognostic_studies Language: En Journal: Neural Netw Journal subject: NEUROLOGIA Year: 2021 Document type: Article