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  Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks

Hu, B., Yu, X., Guan, Z.-H., Kurths, J., Chen, G. (2021): Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks. - IEEE Transactions on Neural Networks and Learning Systems, 32, 5, 2157-2168.
https://doi.org/10.1109/TNNLS.2020.3001009

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 Creators:
Hu, Bin1, Author
Yu, Xinghuo1, Author
Guan, Zhi-Hong1, Author
Kurths, Jürgen2, Author              
Chen, Guanrong1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.

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 Dates: 2021-06-22
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNNLS.2020.3001009
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: IEEE Transactions on Neural Networks and Learning Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 32 (5) Sequence Number: - Start / End Page: 2157 - 2168 Identifier: Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Other: 2162-237X
ISSN: 2162-237X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems