Mean Square Exponential Stability for Uncertain Delayed Stochastic Neural Networks with Markovian Jump Parameters

2010 ◽  
Vol 29 (2) ◽  
pp. 331-348 ◽  
Author(s):  
Qi Zhou ◽  
Bing Chen ◽  
Chong Lin ◽  
Hongyi Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Stochastic Neural Networks ◽  
Mean Square ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Mean Square Exponential Stability

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.

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