scholarly journals Finite-time mixed H∞/passivity for neural networks with mixed interval time-varying delays using the multiple integral Lyapunov -Krasovskii functional

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chalida Phanlert ◽  
Thongchai Botmart ◽  
Wajaree Weera ◽  
Prem Junsawang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Multiple Integral ◽  
Time Varying ◽  
Interval Time

scholarly journals Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meng Hui ◽  
Jiahuang Zhang ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Rui Yao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Complex Valued

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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