Stochastic Exponential Robust Stability of Delayed Complex-Valued Neural Networks With Markova Jumping Parameters | Zendy

Xiaohui Xu | Zendy; Quan Xu | Zendy; Yiqiang Peng | Zendy; Jiye Zhang | Zendy; Yanhai Xu | Zendy

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Stochastic Exponential Robust Stability of Delayed Complex-Valued Neural Networks With Markova Jumping Parameters

Author(s) -

Xiaohui Xu,

Quan Xu,

Yiqiang Peng,

Jiye Zhang,

Yanhai Xu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2776168

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper deals with the problem on stochastic exponential robust stability for a class of complex-valued interval neural networks with Markova jumping parameters and mixed delays, including both time-varying delays and continuously distributed delays. By applying the M-matrix theory and coupling with the vector Lyapunov function method, some sufficient conditions are derived to guarantee the existence, uniqueness, and stochastic exponential robust stability of the equilibrium point of the addressed system. The obtained results not only are easy to judge the dynamical behavior of the addressed system, but also are with lower level conservatism in comparison with some existing results. Finally, two numerical examples with simulation results are given to illustrate the effectiveness of the proposed results.

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