DocumentCode
232053
Title
Global exponential robust stability of stochastic high-order hopfield neural networks with S-type distributed time delays
Author
Xiao Liang
Author_Institution
Coll. of Math. & Syst. Sci., Shandong Univ. of Sci. & Technol., Qingdao, China
fYear
2014
fDate
28-30 July 2014
Firstpage
5118
Lastpage
5124
Abstract
By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the stochastic high-order neural networks with S-type distributed time delays are established, which are easily verifiable and have a wider adaptive. Finally, an example with numerical simulation is given to illustrate the obtained results.
Keywords
Hopfield neural nets; Lyapunov methods; asymptotic stability; delays; numerical analysis; stochastic processes; Lyapunov functional method; S-type distributed time delays; differential inequality technique; global exponential robust stability; numerical simulation; stochastic high-order Hopfield neural networks; Biological neural networks; Control theory; Delay effects; Educational institutions; Robust stability; Robustness; Stochastic processes; Differential inequality; Globally exponentially robustly stable in the mean square sense; High-order; Hopfield neural networks; S-type distributed time delays;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2014 33rd Chinese
Conference_Location
Nanjing
Type
conf
DOI
10.1109/ChiCC.2014.6895811
Filename
6895811
Link To Document