On exponential stability of delayed neural networks with a general class of activation functions

2002 ◽  
Vol 298 (2-3) ◽  
pp. 122-132 ◽  
Author(s):  
Changyin Sun ◽  
Kanjian Zhang ◽  
Shumin Fei ◽  
Chun-Bo Feng
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
General Class ◽  
Activation Functions ◽  
Delayed Neural Networks

Exponential stability of Cohen–Grossberg neural networks with a general class of activation functions

2006 ◽  
Vol 350 (1-2) ◽  
pp. 96-102 ◽  
Author(s):  
Anhua Wan ◽  
Miansen Wang ◽  
Jigen Peng ◽  
Hong Qiao
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
General Class ◽  
Activation Functions

scholarly journals Exponential Stability of Stochastic Delayed Neural Networks with Inverse Hölder Activation Functions and Markovian Jump Parameters

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Yingwei Li ◽  
Huaiqin Wu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Activation Functions ◽  
Markovian Jump ◽  
Delayed Neural Networks ◽  
Markovian Jump Parameters ◽  
Mixed Time Delays ◽  
Finite State

The exponential stability issue for a class of stochastic neural networks (SNNs) with Markovian jump parameters, mixed time delays, andα-inverse Hölder activation functions is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. Firstly, based on Brouwer degree properties, the existence and uniqueness of the equilibrium point for SNNs without noise perturbations are proved. Secondly, by applying the Lyapunov-Krasovskii functional approach, stochastic analysis theory, and linear matrix inequality (LMI) technique, new delay-dependent sufficient criteria are achieved in terms of LMIs to ensure the SNNs with noise perturbations to be globally exponentially stable in the mean square. Finally, two simulation examples are provided to demonstrate the validity of the theoretical results.

GLOBAL EXPONENTIAL STABILITY OF FUZZY INTERVAL DELAYED NEURAL NETWORKS WITH IMPULSES ON TIME SCALES

2009 ◽  
Vol 19 (06) ◽  
pp. 449-456 ◽  
Author(s):  
YONGKUN LI ◽  
TIANWEI ZHANG
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Time Scales ◽  
Global Exponential Stability ◽  
Lyapunov Method ◽  
Delayed Neural Networks ◽  
The Neural Networks ◽  
Uniqueness Of Equilibrium ◽  
Fuzzy Interval

In this paper, we investigate the existence and uniqueness of equilibrium point for fuzzy interval delayed neural networks with impulses on time scales. And we give the criteria of the global exponential stability of the unique equilibrium point for the neural networks under consideration using Lyapunov method. Finally, we present an example to illustrate that our results are effective.

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