State estimation in coupled neural networks with delays via changeable pinning control

This work investigates the state estimation problem in a network of diffusively coupled neural networks (NNs), where each NN has delayed dynamics, and the designer can only measure the states of a portion of NNs. In light of the master–slave synchronization framework, an estimator network of NNs inc...

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Bibliographic Details
Published in:Neural computing & applications 2024, Vol.36 (3), p.1449-1458
Main Authors: Jia, Qiang, Fang, Chengyu, Gao, Yingchao, Yang, Cuili
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Eigenvalues
Image Processing and Computer Vision
Network topologies
Neural networks
Neurons
Original Article
Pinning
Probability and Statistics in Computer Science
State estimation
Synchronism
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Summary:This work investigates the state estimation problem in a network of diffusively coupled neural networks (NNs), where each NN has delayed dynamics, and the designer can only measure the states of a portion of NNs. In light of the master–slave synchronization framework, an estimator network of NNs incorporating pinning control is constructed, and the control gain is assumed to be changeable. Some sufficient criteria are established for successful state estimation, which reveal the influence of NNs’ dynamics, network topology, and time delays. Moreover, some corollaries are also derived to cater several specified cases. Besides, a criterion independent on the exact topological information is also given to tackle scenarios with topological uncertainties. Finally, some numerical examples are provided to demonstrate the validity of theoretical results and the feasibility of control schemes.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09114-7