Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays

2016 ◽  
Vol 75 ◽  
pp. 97-109 ◽  
Author(s):  
Hongfei Li ◽  
Haijun Jiang ◽  
Cheng Hu
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Time Varying ◽  
Bam Neural Networks

scholarly journals Existence and Global Exponential Stability of Periodic Solution to Cohen-Grossberg BAM Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Kaiyu Liu ◽  
Zhengqiu Zhang ◽  
Liping Wang
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Degree Theory ◽  
Multiple Time ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Lipschitz Continuous ◽  
Sign Conditions

We investigate first the existence of periodic solution in general Cohen-Grossberg BAM neural networks with multiple time-varying delays by means of using degree theory. Then using the existence result of periodic solution and constructing a Lyapunov functional, we discuss global exponential stability of periodic solution for the above neural networks. Our result on global exponential stability of periodic solution is different from the existing results. In our result, the hypothesis for monotonicity ineqiality conditions in the works of Xia (2010) Chen and Cao (2007) on the behaved functions is removed and the assumption for boundedness in the works of Zhang et al. (2011) and Li et al. (2009) is also removed. We just require that the behaved functions satisfy sign conditions and activation functions are globally Lipschitz continuous.

Sign in / Sign up

Export Citation Format

Share Document