Paper Info
Title
Some Stability Properties Of Dynamic Neural Networks With Different Time-Scales
Abstract
Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of these networks be stable. The objective of the paper is to develop sufficient conditions for stability of the dynamic neural networks with different time scales. Lyapunov function and singularly perturbed technique are combined to access several new stable properties of different time-scales neural networks. Exponential stability and asymptotic stability are obtained by sector and bound conditions. Compared to other papers, these conditions are simpler. Numerical examples are given to demonstrate the effectiveness of the theoretical results.
Year
DOI
Venue
2006
10.1109/IJCNN.2006.246992
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10
Keywords
Field
DocType
neural network,equilibrium point,equilibrium points,exponential stability,asymptotic stability,lyapunov function
Lyapunov function,Control theory,Computer science,Equilibrium point,Exponential stability,Artificial neural network
Conference
ISSN
Citations 
PageRank 
2161-4393
7
0.60
References 
Authors
12
3
Name
Order
Citations
PageRank
Alejandro Cruz Sandoval1212.48
Wen Yu228322.70
Xiaoou Li355061.95