scholarly journals Robust passivity analysis of uncertain neutral-type neural networks with distributed interval time-varying delay under the effects of leakage delay

2021 ◽  
Vol 26 (03) ◽  
pp. 269-290
Author(s):  
P. Singkibud ◽  
K. Mukdasai
Keyword(s):  
Neural Networks ◽  
Neutral Type ◽  
Leakage Delay ◽  
Time Varying ◽  
Interval Time ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

scholarly journals Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay

2009 ◽  
Vol 2009 ◽  
pp. 1-14 ◽  
Author(s):  
Chien-Yu Lu ◽  
Chin-Wen Liao ◽  
Hsun-Heng Tsai
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Interval Time ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

This paper examines a passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on an appropriate type of Lyapunov functional, sufficient passivity conditions for the DRNNs are derived in terms of a family of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness and applicability.

On passivity analysis for stochastic neural networks with interval time-varying delay

2010 ◽  
Vol 73 (4-6) ◽  
pp. 795-801 ◽  
Author(s):  
Jie Fu ◽  
Huaguang Zhang ◽  
Tiedong Ma ◽  
Qingling Zhang
Keyword(s):  
Neural Networks ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Interval Time ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

Delay-dependent robust stability criteria for stochastic neural networks of neutral-type with interval time-varying delay and linear fractional uncertainties

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
GUOQUAN LIU ◽  
SIMON X. YANG ◽  
YI CHAI ◽  
WEI FU
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Neutral Type ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Interval Time ◽  
Time Varying Delay ◽  
Delay Dependent ◽  
Varying Delay

In this paper, we investigate the problem of robust stability for a class of delayed neural networks of neutral-type with linear fractional uncertainties. The activation functions are assumed to be unbounded, non-monotonic and non-differentiable, and the delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of the interval time-varying delay are available. By constructing a general form of the Lyapunov–Krasovskii functional, and using the linear matrix inequality (LMI) approach, we derive several delay-dependent stability criteria in terms of LMI. Finally, we give a number of examples to illustrate the effectiveness of the proposed method.

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