应数学与统计学院邀请，中国矿业大学博士生导师朱松教授将在我校作学术讲座，欢迎广大师生届时前往。

时间：2021年6月13日(星期日)上午8：30

地点：数学与统计学院会议室

主讲人简介：

朱松，男，1982年生，江苏宿迁人，教授，博士(后)，博士生导师，徐州市工业与应用数学学会秘书长，徐州市统计学会常务理事，校首届优秀青年才俊，校优秀青年骨干教师，获校师德楷模提名奖。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等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人获国家奖学金，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。

   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 equilibrium points (EPs) and 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.

组织单位：数学与统计学院