Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks

Joint Authors

Zhong, Jing
Gao, Zilin
Xiong, Jiang
Liu, Fuming
Liu, Qingshan

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-27

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Biology

Abstract EN

The state observer for dynamic links in complex dynamical networks (CDNs) is investigated by using the adaptive method whether the networks are undirected or directed.

In this paper, a complete network model is proposed, which is composed of two coupled subsystems called nodes subsystem and links subsystem, respectively.

Especially, for the links subsystem, associated with some assumptions, the state observer with parameter adaptive law is designed.

Compared to the existing results about the state observer design of CDNs, the advantage of this method is that a estimation problem of dynamic links is solved in directed networks for the first time.

Finally, the results obtained in this paper are demonstrated by performing a numerical example.

American Psychological Association (APA)

Gao, Zilin& Xiong, Jiang& Zhong, Jing& Liu, Fuming& Liu, Qingshan. 2020. Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138895

Modern Language Association (MLA)

Gao, Zilin…[et al.]. Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1138895

American Medical Association (AMA)

Gao, Zilin& Xiong, Jiang& Zhong, Jing& Liu, Fuming& Liu, Qingshan. Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138895

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1138895