Title :
Global exponential robust stability of stochastic high-order hopfield neural networks with S-type distributed time delays
Author_Institution :
Coll. of Math. & Syst. Sci., Shandong Univ. of Sci. & Technol., Qingdao, China
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;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6895811
