Exponential estimates and exponential stability for neutral-type neural networks with multiple delays

2015 ◽  
Vol 149 ◽  
pp. 868-883 ◽  
Author(s):  
Xiaofeng Liao ◽  
Yilu Liu ◽  
Huiwei Wang ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Neutral Type ◽  
Multiple Delays ◽  
Exponential Estimates

scholarly journals The Existence and Global Exponential Stability of Almost Periodic Solutions for Neutral-Type CNNs on Time Scales

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 321 ◽  
Author(s):  
Bing Li ◽  
Yongkun Li ◽  
Xiaofang Meng
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Periodic Solutions ◽  
Time Scales ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays

In this paper, neutral-type competitive neural networks with mixed time-varying delays and leakage delays on time scales are proposed. Based on the contraction fixed-point theorem, some sufficient conditions that are independent of the backwards graininess function of the time scale are obtained for the existence and global exponential stability of almost periodic solutions of neural networks under consideration. The results obtained are brand new, indicating that the continuous time and discrete-time conditions of the network share the same dynamic behavior. Finally, two examples are given to illustrate the validity of the results obtained.

EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS

2010 ◽  
Vol 24 (11) ◽  
pp. 1099-1110 ◽  
Author(s):  
RATHINASAMY SAKTHIVEL ◽  
R. SAMIDURAI ◽  
S. MARSHAL ANTHONI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exponential Stability ◽  
Stochastic Analysis ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
Impulsive Effects ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Matrix Inequality

This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.

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