Novel results on passivity and exponential passivity for multiple discrete delayed neutral-type neural networks with leakage and distributed time-delays

2018 ◽  
Vol 115 ◽  
pp. 268-282 ◽  
Author(s):  
C. Maharajan ◽  
R. Raja ◽  
Jinde Cao ◽  
G. Rajchakit ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Neutral Type ◽  
Distributed Time Delays

Mode-dependent projective synchronization for neutral-type neural networks with distributed time-delays

2014 ◽  
Vol 140 ◽  
pp. 97-103 ◽  
Author(s):  
Qingyu Zhu ◽  
Wuneng Zhou ◽  
Liuwei Zhou ◽  
Mingqi Wu ◽  
Dongbing Tong
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Neutral Type ◽  
Projective Synchronization ◽  
Distributed Time Delays

scholarly journals Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Haiyong Zheng ◽  
Bin Wu ◽  
Tengda Wei ◽  
Linshan Wang ◽  
Yangfan Wang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Differential Inequality ◽  
Time Delays ◽  
Functional Method ◽  
High Order ◽  
Distributed Time Delays ◽  
Inequality Technique ◽  
Global Exponential Robust Stability ◽  
Lyapunov Functional Method

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope.

scholarly journals Design of exponential state estimators for neutral-type neural networks with mixed time delays

Filomat ◽  
2016 ◽  
Vol 30 (13) ◽  
pp. 3435-3449
Author(s):  
Bo Du
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Estimation Error ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
The State ◽  
Linear Matrix ◽  
Mixed Time Delays ◽  
State Estimators ◽  
Exponentially Stable

In this paper, the state estimation problem is dealt with for a class of neutral-type neural networks with mixed time delays. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.

scholarly journals Switched Exponential State Estimation and Robust Stability for Interval Neural Networks with Discrete and Distributed Time Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Hongwen Xu ◽  
Huaiqin Wu ◽  
Ning Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
State Estimation ◽  
Time Delays ◽  
Estimation Error ◽  
Linear Matrix ◽  
Robust Exponential Stability ◽  
Interval Neural Networks ◽  
Distributed Time Delays ◽  
Error System

The interval exponential state estimation and robust exponential stability for the switched interval neural networks with discrete and distributed time delays are considered. Firstly, by combining the theories of the switched systems and the interval neural networks, the mathematical model of the switched interval neural networks with discrete and distributed time delays and the interval estimation error system are established. Secondly, by applying the augmented Lyapunov-Krasovskii functional approach and available output measurements, the dynamics of estimation error system is proved to be globally exponentially stable for all admissible time delays. Both the existence conditions and the explicit characterization of desired estimator are derived in terms of linear matrix inequalities (LMIs). Moreover, a delay-dependent criterion is also developed, which guarantees the robust exponential stability of the switched interval neural networks with discrete and distributed time delays. Finally, two numerical examples are provided to illustrate the validity of the theoretical results.

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