Second-order reciprocally convex approach for stability of neural networks with interval time-varying delays

2017 ◽  
Author(s):  
Chunxia Li ◽  
Fang Qiu
Keyword(s):  
Neural Networks ◽  
Second Order ◽  
Time Varying ◽  
Interval Time ◽  
Reciprocally Convex Approach

Synchronization criteria for coupled neural networks with interval time-varying delays and leakage delay

2012 ◽  
Vol 218 (12) ◽  
pp. 6762-6775 ◽  
Author(s):  
M.J. Park ◽  
O.M. Kwon ◽  
Ju H. Park ◽  
S.M. Lee ◽  
E.J. Cha
Keyword(s):  
Neural Networks ◽  
Leakage Delay ◽  
Time Varying ◽  
Interval Time ◽  
Coupled Neural Networks

scholarly journals Robust Synchronization Criterion for Coupled Stochastic Discrete-Time Neural Networks with Interval Time-Varying Delays, Leakage Delay, and Parameter Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
M. J. Park ◽  
O. M. Kwon ◽  
Ju H. Park ◽  
S. M. Lee ◽  
E. J. Cha
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Leakage Term ◽  
Interval Time ◽  
Robust Synchronization ◽  
Delay Dependent ◽  
Finsler’S Lemma

The purpose of this paper is to investigate a delay-dependent robust synchronization analysis for coupled stochastic discrete-time neural networks with interval time-varying delays in networks coupling, a time delay in leakage term, and parameter uncertainties. Based on the Lyapunov method, a new delay-dependent criterion for the synchronization of the networks is derived in terms of linear matrix inequalities (LMIs) by constructing a suitable Lyapunov-Krasovskii’s functional and utilizing Finsler’s lemma without free-weighting matrices. Two numerical examples are given to illustrate the effectiveness of the proposed methods.

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