期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 6, 页码 5367-5379出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3031309
关键词
Cellular neural networks (CNNs); global exponential stability (GES); matrix theory; memrisor; multistability; time delays
类别
资金
- National Natural Science Foundation of China [61873271, 61873272]
- Fundamental Research Funds for the Central Universities [2018XKQYMS15]
- Double-First-Rate Special Fund for Construction of China University of Mining and Technology [2018ZZCX14]
Dynamic memristor-cellular neural networks replace linear resistors with flux-controlled memristors in each cell, providing the advantage of disappearing voltages, currents, and power consumption when reaching a steady state. Previous studies on stability rarely considered time delay, which has a significant impact on system stability. By extending the original system to a DM-D(delay)CNNs model and using Lyapunov method and matrix theory, new sufficient conditions for global asymptotic and exponential stability of DM-DCNNs with a known convergence rate are obtained. The results show the potential applications and advantages of DM-DCNNs in various fields.
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 3(n) equilibrium points (EPs) and 2(n) 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据