Existence, uniqueness and global stability of Clifford-valued neutral-type neural networks with time delays

2021 ◽  
Author(s):  
G. Rajchakit ◽  
R. Sriraman ◽  
C.P. Lim ◽  
B. Unyong
Keyword(s):  
Neural Networks ◽  
Global Stability ◽  
Time Delays ◽  
Neutral Type

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 Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based onM-Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Wuneng Zhou ◽  
Xueqing Yang ◽  
Jun Yang ◽  
Jun Zhou
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Neutral Type ◽  
Linear Matrix ◽  
Control Law ◽  
Multiple Time ◽  
Matrix Inequality ◽  
Stochastic Disturbance ◽  
Stochastic Synchronization ◽  
Multiple Time Delays

The problem of stochastic synchronization of neutral-type neural networks with multidelays based onM-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional andM-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switching are stochastic synchronization. The synchronization condition is expressed as linear matrix inequality which can be easily solved by MATLAB. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper.

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