Synchronization of Coupled Memristive Neural Network Based on Edge-Event Triggered Control

被引:0
作者
Letian An
Yongqing Yang
Rixu Hao
Li Li
机构
[1] Jiangnan University,School of Science
[2] Jiangnan University,School of Internet of Things
来源
Neural Processing Letters | 2023年 / 55卷
关键词
Edge-event triggered control protocol; Synchronization; Coupled memristive neural networks; Zeno behavior;
D O I
暂无
中图分类号
学科分类号
摘要
This research aims to investigate synchronization issues in coupled memristive neural networks (CMNNs) using both the static and dynamic edge-event triggered control protocols. An interval parameter system is developed by integrating the concept of Filippov solution with differential inclusion theory. Unlike existing work, the suggested edge-event triggered mechanisms don’t require the constant information transfer among neighboring nodes, providing a more distributed control approach that reduces system resources since each node communicates asynchronously. Additionally the absence of Zeno behavior at any given moment supports the efficacy of the approach. To demonstrate its viability, a practical simulation example is presented.
引用
收藏
页码:11209 / 11232
页数:23
相关论文
共 136 条
  • [1] Lu P(2022)An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network Chin J Aeronaut Astronaut (Engl Vers) 4 102-117
  • [2] Wang N(2022)A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification Chin J Aeronaut Astronaut (Engl Vers) 4 344-361
  • [3] Chang X(2022)Determination of quantum toric error correction code threshold using convolutional neural network decoders Chin Phys B (Engl Vers) 31 156-163
  • [4] Zhou D(2022)FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient Chin Phys B (Engl Vers) 31 120-130
  • [5] Zhuang X(2022)Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: analysis of image encryption technique Math Meth Appl Sci 39 385-392
  • [6] Zuo H(2003)A new chaotic parameters disturbance annealing neural network for solving global optimization problems Theoret Phys Newsl (Engl Vers) 15 39-43
  • [7] Wang H(2008)Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods J Cent South Univ Technol (Engl Vers) 3 12-15
  • [8] Xue Y(2005)Morphological self-organizing feature map neural network with applications to automatic target recognition China Opt Expr (Engl Vers) 16 1261-1275
  • [9] Ma Y(2003)Network participation indices: characterizing component roles for information processing in neural networks Neural Netw: Off J Int Neural Netw Soc 9 293-308
  • [10] Yu F(2012)A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network Biogeosciences 18 507-519