Some generalized global stability criteria for delayed Cohen–Grossberg neural networks of neutral-type

2019 ◽  
Vol 116 ◽  
pp. 198-207 ◽  
Author(s):  
Ruya Samli ◽  
Sibel Senan ◽  
Eylem Yucel ◽  
Zeynep Orman
Keyword(s):  
Neural Networks ◽  
Global Stability ◽  
Neutral Type ◽  
Stability Criteria

On Robust Stability for Stochastic Neural Networks of Neutral-Type with Uncertainties and Time-Varying Delays

2012 ◽  
Vol 457-458 ◽  
pp. 716-722
Author(s):  
Guo Quan Liu ◽  
Simon X. Yang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Neutral Type ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Analysis Problem ◽  
Analysis Technique ◽  
Robust Stability Analysis

This paper is concerned with the robust stability analysis problem for stochastic neural networks of neutral-type with uncertainties and time-varying delays. Novel stability criteria are proposed in terms of linear matrix inequality (LMI) by defining a Lyapunov-Krasovskii functional and using the stochastic analysis technique. Two examples are given to show the effectiveness of the obtained conditions.

scholarly journals Global Exponential Stability of Discrete-Time Neural Networks with Time-Varying Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
S. Udpin ◽  
P. Niamsup
Keyword(s):  
Neural Networks ◽  
Global Stability ◽  
Discrete Time ◽  
Type Inequality ◽  
Discrete Systems ◽  
Nonlinear Difference Equation ◽  
Time Varying ◽  
Stability Criteria ◽  
Nonlinear Discrete Systems ◽  
Theoretical Results

This paper presents some global stability criteria of discrete-time neural networks with time-varying delays. Based on a discrete-type inequality, a new global stability condition for nonlinear difference equation is derived. We consider nonlinear discrete systems with time-varying delays and independence of delay time. Numerical examples are given to illustrate the effectiveness of our theoretical results.

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