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朱松教授学术报告会


发表日期:2021-01-04 供稿:

朱松教授学术报告会

 

报告题目:New Criteria on Stability of Dynamic Memristor Delayed Cellular Neural Networks

报 告 人:朱松教授(中国矿业大学)

    间:202115日(星期二)下午230

    点:大赢家足球(磬苑校区)计算智能与信号处理教育部重点实验室(老研究生院二楼会议室)

 

报告摘要:Dynamic memristor (DM)-cellular neural networks (CNNs), which replace a linear resistor with flux-controlled memristor in the architecture of each cell of traditional CNNs, have attracted researchers’ attention. Compared with common neural networks, the DM-CNNs have an outstanding merit: when a steady state is reached, all voltages, currents, and power consumption of DM-CNNs disappeared, in the meantime, the memristor can store the computation results by serving as nonvolatile memories. The previous study on stability of DM-CNNs rarely considered time delay, while delay is quite common and highly impacts the stability of the system. Thus, taking the time delay effect into consideration, we extend the original system to DM-D(delay)CNNs model. By using the Lyapunov method and the matrix theory, some new sufficient conditions for the global asymptotic stability and global exponential stability with a known convergence rate of DM-DCNNs are obtained. These criteria generalized some known conclusions and are easily verified. Moreover, we find DM-DCNNs have 3n equilibrium points (EPs) and 2n of them are locally asymptotically stable. These results are obtained via a given constitutive relation of memristor and the appropriate division of state space. Combine with these theoretical results, the applications of DM-DCNNs can be extended to other fields, such as associative memory, and its advantage can be used in a better way. Finally, numerical simulations are offered to illustrate the effectiveness of our theoretical results.

 

 

主办单位:计算智能与信号处理教育部重点实验室、大赢家足球 

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科学技术处

202114

 

报告人简介:朱松,男,1982年生,江苏宿迁人,教授,博士(),博士生导师,徐州市工业与应用数学学会秘书长,校首届优秀青年才。S判闱嗄旯歉山淌。主持国家自然科学基金面上项目,青年项目等10余项;在国际期刊 IEEE TNNLS,TCYB,TSMC:Systems,IJRNC,IJC,NN以第一作者或通讯作者发表学术论文80余篇,其中SCI论文60余篇,指导学生6次获国家奖学金,1人获江苏省优秀硕士学位论文。研究方向:随机系统稳定性理论、神经网络、忆阻器、流体网络。

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