The LMI method for stationary oscillation of interval neural networks with three neuron activations under impulsive effects

2013 ◽  
Vol 14 (3) ◽  
pp. 1404-1416 ◽  
Author(s):  
Fangfang Jiang ◽  
Jianhua Shen ◽  
Xiaodi Li
Keyword(s):  
Neural Networks ◽  
Impulsive Effects ◽  
Stationary Oscillation ◽  
Interval Neural Networks

scholarly journals Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guiying Chen ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Leakage Term ◽  
Interval Neural Networks ◽  
Global Exponential Robust Stability ◽  
The Stability ◽  
Delay Differential ◽  
Delay Differential Inequality

The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method ofM-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results.

Existence of Anti-Periodic Solution for Delayed Cellular Neural Networks with Impulsive Effects

2013 ◽  
Vol 477-478 ◽  
pp. 1499-1503
Author(s):  
Lin Li Zhang ◽  
Rui Li Fan ◽  
An Ping Liu ◽  
Wei Fang Yang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Periodic Solution ◽  
Existence And Uniqueness ◽  
Cellular Neural Networks ◽  
Existence Of Solution ◽  
Impulsive Effects ◽  
Iterative Analysis

As an important tool to study practical problems of biology, engineering and image processing, the cellular neural networks (CNNs) has caused more and more attention. Some interesting results about the existence of solution for cellular neural networks have been obtained. In this paper, by means of iterative analysis, the existence and uniqueness of anti-periodic solution of delayed cellular neural networks with impulsive effects are considered. Some new results are obtained.

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