Cluster Output Synchronization for Memristive Neural Networks

Chao Zhou, Chunhua Wang, Yichuang Sun, Wei Yao, Hairong Lin

Research output: Contribution to journalArticlepeer-review

21 Downloads (Pure)


Herein, cluster output synchronization for memristive neural networks (MNNs) is investigated using two different control schemes. Existing synchronization models for MNNs focus on the behavior of a single neuron node in one-cluster networks. However, actual neural networks (NNs) are clustered organizations consisting of multiple interacting clusters, where the nodes from the same cluster combine and work together. This study proposes a cluster output synchronization model for MNNs, which considers the combination output behavior of the nodes in NNs clusters. Accordingly, two specific control schemes are designed: one based on feedback control involves designing a small number of controllers to reduce control costs, and the other based on adaptive control involves designing multiple adjustable controllers to increase the anti-interference capacity of the control system. Meanwhile, to facilitate synchronization in MNNs, a model relationship between MNNs and traditional NNs is investigated. By utilizing the control schemes, model relationship, and Lyapunov stability theory, sufficient conditions are obtained for validating the cluster output synchronization. Finally, several numerical examples are given to illustrate the accuracy of the theoretical results.
Original languageEnglish
Pages (from-to)459-477
Number of pages19
JournalInformation Sciences
Early online date30 Dec 2021
Publication statusPublished - 1 Apr 2022


  • Cluster synchronization
  • Memristive neural networks
  • Model relationship
  • Output synchronization


Dive into the research topics of 'Cluster Output Synchronization for Memristive Neural Networks'. Together they form a unique fingerprint.

Cite this