TY - JOUR
T1 - Cluster Output Synchronization for Memristive Neural Networks
AU - Zhou, Chao
AU - Wang, Chunhua
AU - Sun, Yichuang
AU - Yao, Wei
AU - Lin, Hairong
N1 - Funding Information:
This work is supported by The Major Research Project of the National Natural Science Foundation of China (91964108), The National Natural Science Foundation of China (61971185), The Natural Science Foundation of Hunan Province (2020JJ4218), and The Open Fund Project of Key Laboratory in Hunan Universities (18K010). Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
KW - Cluster synchronization
KW - Memristive neural networks
KW - Model relationship
KW - Output synchronization
UR - http://www.scopus.com/inward/record.url?scp=85122627427&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2021.12.084
DO - 10.1016/j.ins.2021.12.084
M3 - Article
SN - 0020-0255
VL - 589
SP - 459
EP - 477
JO - Information Sciences
JF - Information Sciences
ER -