Improved stochastic synchronization criterion of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities

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• 英文论文题名：Improved stochastic synchronization criterion of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities
• 论文作者：Deqiang Zeng ; Ruimei Zhang ; Kaibo Shi ; Shouming Zhong
• 英文论文作者：Deqiang Zeng ; Ruimei Zhang ; Kaibo Shi ; Shouming Zhong ; Data Recovery Key Laboratory of Sichuan Province ; College of Mathematics and Information Science ; Neijiang Normal University ; School of Mathematics Sciences ; University of Electronic Science and Technology of China ; School of Information Sciences and Engineering ; Chengdu University
• 年：2017
• 作者机构：Data Recovery Key Laboratory of Sichuan Province,College of Mathematics and Information Science,Neijiang Normal University;School of Mathematics Sciences,University of Electronic Science and Technology of China;School of Information Sciences and Engineering,Chengdu University;
• 英文论文关键词：Markovian coupled neural networks ; Stochastic synchronization ; Random coupling strengths ; Partial information on transition probabilities
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TP183
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：6
• 文件大小：207k
• 原文格式：D
• 会议级别：全国

摘要

This paper investigates the synchronization problem of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities. In this coupled neural network model, each transition rate is only known partial information, and the coupling strengths are characterized by mutually independent random variables.By constructing a new augmented Lyapunov-Krasovskii functional(LKF) and reciprocal convex combination, synchronization criterion is derived. In comparison with existing results, the established criterion is not only less conservative but with less computational burden. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed criterion.
This paper investigates the synchronization problem of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities. In this coupled neural network model, each transition rate is only known partial information, and the coupling strengths are characterized by mutually independent random variables.By constructing a new augmented Lyapunov-Krasovskii functional(LKF) and reciprocal convex combination, synchronization criterion is derived. In comparison with existing results, the established criterion is not only less conservative but with less computational burden. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed criterion.

引文

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