A Robust Nonlinear Observer for a Class of Neural Mass Models
Joint Authors
Miao, Dongkai
Gao, Qing
Liu, Xian
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-20
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
A new method of designing a robust nonlinear observer is presented for a class of neural mass models by using the Lur’e system theory and the projection lemma.
The observer is robust towards input uncertainty and measurement noise.
It is applied to estimate the unmeasured membrane potential of neural populations from the electroencephalogram (EEG) produced by the neural mass models.
An illustrative example shows the effectiveness of the proposed method.
American Psychological Association (APA)
Liu, Xian& Miao, Dongkai& Gao, Qing. 2014. A Robust Nonlinear Observer for a Class of Neural Mass Models. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1048774
Modern Language Association (MLA)
Liu, Xian…[et al.]. A Robust Nonlinear Observer for a Class of Neural Mass Models. The Scientific World Journal No. 2014 (2014), pp.1-5.
https://search.emarefa.net/detail/BIM-1048774
American Medical Association (AMA)
Liu, Xian& Miao, Dongkai& Gao, Qing. A Robust Nonlinear Observer for a Class of Neural Mass Models. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1048774
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048774
