Title :
Novel Exponential Stability Criteria of High-Order Neural Networks With Time-Varying Delays
Author :
Zheng, Cheng-De ; Zhang, Huaguang ; Wang, Zhanshan
Author_Institution :
Dept. of Math., Dalian Jiaotong Univ., Dalian, China
fDate :
4/1/2011 12:00:00 AM
Abstract :
The global exponential stability is analyzed for a class of high-order Hopfield-type neural networks with time-varying delays. Based on the Lyapunov stability theory, together with the linear matrix inequality approach and free-weighting matrix method, some less conservative delay-independent and delay-dependent sufficient conditions are presented for the global exponential stability of the equilibrium point of the considered neural networks. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.
Keywords :
Hopfield neural nets; Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; time-varying systems; Lyapunov stability; exponential stability; free weighting matrix; high order Hopfield neural network; linear matrix inequality; time varying delays; Asymptotic stability; Delay effects; Educational institutions; Hopfield neural networks; Linear matrix inequalities; Lyapunov method; Neural networks; Robust stability; Stability analysis; Stability criteria; Free-weighting matrix method; global exponential stability; high-order neural networks; linear matrix inequality (LMI); Algorithms; Computer Simulation; Decision Support Techniques; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2010.2059010
