Combined Convex Technique on Delay-Dependent Stability for Delayed Neural Networks

2013 ◽  
Vol 24 (9) ◽  
pp. 1459-1466 ◽  
Author(s):  
Tao Li ◽  
Ting Wang ◽  
Aiguo Song ◽  
Shumin Fei
Keyword(s):  
Neural Networks ◽  
Delayed Neural Networks ◽  
Delay Dependent ◽  
Delay Dependent Stability

Exponential Stability Analysis for Neutral Stochastic Delayed Neural Networks

2011 ◽  
Vol 354-355 ◽  
pp. 877-880
Author(s):  
Min Gang Hua ◽  
Jun Tao Fei ◽  
Wei Li Dai
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Lyapunov Functional ◽  
Stability Criteria ◽  
Delayed Neural Networks ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability

In this paper, the generalized Finsler lemma and augmented Lyapunov functional are introduced to establish some improved delay-dependent stability criteria of neutral stochastic delayed neural networks. The stability criteria in the new results improve and generalize existing ones. Two examples are included to show the effectiveness of the results.

scholarly journals Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ding ◽  
Hong-Bing Zeng ◽  
Wei Wang ◽  
Fei Yu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Improved Stability ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability ◽  
Varying Delay

This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.

scholarly journals Robust State Estimation for Delayed Neural Networks with Stochastic Parameter Uncertainties

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
M. J. Park ◽  
O. M. Kwon ◽  
Ju H. Park ◽  
S. M. Lee ◽  
E. J. Cha
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Delayed Neural Networks ◽  
Stochastic Parameter ◽  
Mean And Variance ◽  
Delay Dependent ◽  
Designing Method ◽  
Dependent State

This paper considers the problem of delay-dependent state estimation for neural networks with time-varying delays and stochastic parameter uncertainties. It is assumed that the parameter uncertainties are affected by the environment which is changed with randomly real situation, and its stochastic information such as mean and variance is utilized in the proposed method. By constructing a newly augmented Lyapunov-Krasovskii functional, a designing method of estimator for neural networks is introduced with the framework of linear matrix inequalities (LMIs) and a neural networks model with stochastic parameter uncertainties which have not been introduced yet. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea.

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