scholarly journals Stability Analysis for Stochastic Neutral-Type Memristive Neural Networks with Time-Varying Delay and S-Type Distributed Delays

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Changjian Wang ◽  
Zuoliang Xiong ◽  
Min Liang ◽  
Hongwei Yin
Keyword(s):  
Neural Networks ◽  
Stochastic Analysis ◽  
Neutral Type ◽  
Distributed Delays ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Time Varying Delay ◽  
System A ◽  
Analysis Theory ◽  
Varying Delay

In this paper, we consider the input-to-stability for a class of stochastic neutral-type memristive neural networks. Neutral terms and S-type distributed delays are taken into account in our system. Using the stochastic analysis theory and Itô formula, we obtain the conditions of mean-square exponential input-to-stability for system. A numerical example is given to illustrate the correctness of our conclusions.

scholarly journals Input-to-State Stability of Stochastic Memristive Neural Networks with Time-Varying Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xu Y. Lou ◽  
Qian Ye
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Mean Square ◽  
Memristive Neural Networks ◽  
Time Varying Delay ◽  
Analysis Theory ◽  
Mean Square Exponential Stability ◽  
The Neural Networks ◽  
Varying Delay

This paper is concerned with the input-to-state stability problem of a class of memristive neural networks. We consider the neural networks that take into account both the stochastic effects and time-varying delay, and introduce the notions of meansquare exponential input-to-state stability. Using the stochastic analysis theory and Itô formula for stochastic differential equations, we establish sufficient conditions for both mean-square exponential input-to-state stability and mean-square exponential stability. Numerical simulations are also provided to demonstrate the theoretical results.

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