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Simplifying Complex Network Stability Analysis via Hierarchical Node Aggregation and Optimal Periodic Control.

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Název: Simplifying Complex Network Stability Analysis via Hierarchical Node Aggregation and Optimal Periodic Control.
Autoři: Xiong, Wenjun, Yu, Xinghuo, Liu, Chen, Wen, Guanghui, Wen, Shiping
Zdroj: IEEE Transactions on Neural Networks & Learning Systems; Jul2021, Vol. 32 Issue 7, p3098-3107, 10p
Témata: ALGORITHMS, HEURISTIC algorithms
Abstrakt: In this study, the stability of a hierarchical network with delayed output is discussed by applying a kind of optimal periodic control. To reduce the number of the nodes of the original hierarchical network, an aggregation algorithm is first presented to take some nodes with the same information as an aggregated node. Furthermore, the stability of the original hierarchical network can be guaranteed by the optimal periodic control of the aggregated hierarchical network. Then, an optimal control scheme is proposed to reduce the bandwidth waste in information transmission. In the control scheme, the time sequence is separated into two parts: the deterministic segment and the dynamic segment. With the optimal control scheme, two targets are achieved: 1) the outputs of the original and aggregated hierarchical system are both asymptotically stable and 2) the nodes with slow convergent rate can catch up with the convergence speeds of other nodes. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Biomedical Index
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  Data: Simplifying Complex Network Stability Analysis via Hierarchical Node Aggregation and Optimal Periodic Control.
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  Data: <searchLink fieldCode="AR" term="%22Xiong%2C+Wenjun%22">Xiong, Wenjun</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Xinghuo%22">Yu, Xinghuo</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chen%22">Liu, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Wen%2C+Guanghui%22">Wen, Guanghui</searchLink><br /><searchLink fieldCode="AR" term="%22Wen%2C+Shiping%22">Wen, Shiping</searchLink>
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  Data: IEEE Transactions on Neural Networks & Learning Systems; Jul2021, Vol. 32 Issue 7, p3098-3107, 10p
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  Label: Abstract
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  Data: In this study, the stability of a hierarchical network with delayed output is discussed by applying a kind of optimal periodic control. To reduce the number of the nodes of the original hierarchical network, an aggregation algorithm is first presented to take some nodes with the same information as an aggregated node. Furthermore, the stability of the original hierarchical network can be guaranteed by the optimal periodic control of the aggregated hierarchical network. Then, an optimal control scheme is proposed to reduce the bandwidth waste in information transmission. In the control scheme, the time sequence is separated into two parts: the deterministic segment and the dynamic segment. With the optimal control scheme, two targets are achieved: 1) the outputs of the original and aggregated hierarchical system are both asymptotically stable and 2) the nodes with slow convergent rate can catch up with the convergence speeds of other nodes. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jul2021
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