Sampled-data synchronisation for memristive neural networks with multiple time-varying delays via extended convex combination method

2018 ◽  
Vol 12 (7) ◽  
pp. 922-932 ◽  
Author(s):  
Ruimei Zhang ◽  
Deqiang Zeng ◽  
Shouming Zhong ◽  
Yongbin Yu ◽  
Jun Cheng
Keyword(s):  
Neural Networks ◽  
Convex Combination ◽  
Combination Method ◽  
Multiple Time ◽  
Time Varying ◽  
Sampled Data ◽  
Memristive Neural Networks ◽  
Convex Combination Method

scholarly journals Analysis on Global Asymptotical Stability of Genetic Regulatory Networks with Time-Varying Delays via Convex Combination Method

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Yang Liu ◽  
Haixia Wu
Keyword(s):  
Stability Criterion ◽  
Regulatory Networks ◽  
Convex Combination ◽  
Genetic Regulatory Networks ◽  
Asymptotical Stability ◽  
Combination Method ◽  
Experimental Result ◽  
Time Varying ◽  
Global Asymptotical Stability ◽  
Convex Combination Method

The global asymptotical stability analysis for genetic regulatory networks with time delays is concerned. By using Lyapunov functional theorem, LMIs, and convex combination method, a new delay-dependent stability criterion has been presented in terms of LMIs to guarantee the delayed genetic regulatory networks to be asymptotically stable. The restriction that the derivatives of the time-varying delays are less than one is removed. Our result is applicable to both fast and slow time-varying delays. The stability criterion has less conservative and wider application range. Experimental result has been used to demonstrate the usefulness of the main results and less conservativeness of the proposed method.

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