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
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;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2000.912879
