Robust Stability and Synchronization of Neural Networks

2015 ◽  
pp. 37-91
Author(s):  
Wuneng Zhou ◽  
Jun Yang ◽  
Liuwei Zhou ◽  
Dongbing Tong
Keyword(s):  
Neural Networks ◽  
Robust Stability

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.

Robust Stability of Inertial BAM Neural Networks with Time Delays and Uncertainties via Impulsive Effect

2017 ◽  
Vol 48 (1) ◽  
pp. 245-256 ◽  
Author(s):  
Wei Zhang ◽  
Tingwen Huang ◽  
Chuandong Li ◽  
Jing Yang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Time Delays ◽  
Impulsive Effect ◽  
Bam Neural Networks

scholarly journals LMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yangfan Wang ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Robust Stability ◽  
High Order ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Global Exponential Robust Stability

This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.

Robust stability analysis for delayed Cohen-Grossberg-type bidirectional associative memory neural networks with norm-bounded uncertainties

2009 ◽  
Vol 223 (5) ◽  
pp. 693-707
Author(s):  
Y Wang ◽  
P Hu
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Bidirectional Associative Memory ◽  
Parameter Uncertainties ◽  
Robust Stability Analysis ◽  
Global Robust Stability ◽  
Bounded Uncertainties

In this paper, the problem of global robust stability is discussed for uncertain Cohen-Grossberg-type (CG-type) bidirectional associative memory (BAM) neural networks (NNs) with delays. The parameter uncertainties are supposed to be norm bounded. The sufficient conditions for global robust stability are derived by employing a Lyapunov-Krasovskii functional. Based on these, the conditions ensuring global asymptotic stability without parameter uncertainties are established. All conditions are expressed in terms of linear matrix inequalities (LMIs). In addition, two examples are provided to illustrate the effectiveness of the results obtained.

Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays

Complexity ◽  
2014 ◽  
Vol 21 (5) ◽  
pp. 59-72 ◽  
Author(s):  
Chandrasekar Pradeep ◽  
Arunachalam Chandrasekar ◽  
Rangasamy Murugesu ◽  
Rajan Rakkiyappan
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Stability Analysis
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