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

Yingwei Li | Zendy; Huaiqin Wu | Zendy

Open Access

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

Author(s) -

Yingwei Li,

Huaiqin Wu

Publication year - 2014

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2014/784107

Subject(s) - uniqueness , exponential stability , mathematics , markov chain , stability (learning theory) , inverse , exponential function , artificial neural network , equilibrium point , computer science , mathematical analysis , differential equation , artificial intelligence , statistics , machine learning , physics , geometry , quantum mechanics , nonlinear system

The exponential stability issue for a class of stochastic neural networks (SNNs) with Markovian jump parameters, mixed time delays, and α-inverse Hölder activation functions is investigated. The jumping parameters are modeledas a continuous-time finite-state Markov chain. Firstly, based on Brouwer degreeproperties, 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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