DocumentCode
1743895
Title
Global asymptotic stability of a class of dynamic neural systems with asymmetric connection weights
Author
Xia, Youshen ; Wang, Jun
Author_Institution
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China
Volume
1
fYear
2000
fDate
2000
Firstpage
870
Abstract
Recently, a class of dynamic neural systems has been presented and analyzed due to their good performance in optimization computation and low complexity for implementation. The global asymptotic stability of dynamic neural systems with symmetric weights has been well studied. In this paper, we investigate the global asymptotic stability of a dynamic neural system with asymmetric weights. Since asymmetric weight cases are more general than symmetric ones, the new results are significant both in theory and applications
Keywords
Hopfield neural nets; asymptotic stability; matrix algebra; neurocontrollers; Hopfield neural nets; asymmetric connection weights; asymptotic stability; dynamic neural systems; matrix algebra; Asymptotic stability; Automation; Circuit stability; Councils; Linear systems; Neural networks; Neurons; Piecewise linear techniques; Symmetric matrices;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location
Sydney, NSW
ISSN
0191-2216
Print_ISBN
0-7803-6638-7
Type
conf
DOI
10.1109/CDC.2000.912879
Filename
912879
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