Global asymptotic and exponential stability of a dynamic neural system with asymmetric connection weights

Youshen Xia; Jun Wang

doi:10.1109/9.917666

Author(s)

Youshen Xia
Jun Wang

Detail(s)

Original language	English
Pages (from-to)	635-638
Journal / Publication	IEEE Transactions on Automatic Control
Volume	46
Issue number	4
Publication status	Published - Apr 2001
Externally published	Yes

Link(s)

DOI	DOI https://doi.org/10.1109/9.917666 Final Published version
	Check@CityULib
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-0035307304&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(3a0347f2-fa6c-49d7-9299-1abc4ec7adfc).html

Abstract

Recently, a dynamic neural system was presented and analyzed due to its good performance in optimization computation and low complexity for implementation. The global asymptotic stability of such a dynamic neural system with symmetric connection weights was well studied. In this note, based on a new Lyapunov function, we investigate the global asymptotic stability of the dynamic neural system with asymmetric connection weights. Since the dynamic neural system with asymmetric weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially, the new result can cover the asymptotic stability results of linear systems as special cases.