Paper Info
Title
Passivity analysis for neuro identifier with different time-scales
Abstract
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.
Year
DOI
Venue
2006
10.1007/11816157_51
ICIC (1)
Keywords
Field
DocType
bounded input,passivity analysis,certain sense,dynamic neural network,neural identifer,asymptotic stability,bounded output stability,stability property,neuro identifier,different time-scales,passivity-based approach,input-to-state stability,stability condition,nonlinear system
Passivity,Nonlinear system,Physical system,Control theory,Computer science,Stability conditions,Exponential stability,Artificial neural network,Dynamical system,Bounded function
Conference
Volume
ISSN
ISBN
4113
0302-9743
3-540-37271-7
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Alejandro Cruz Sandoval1212.48
Wen Yu228322.70
Xiaoou Li355061.95