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Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties

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posted on 2020-05-01, 00:00 authored by P Chanthorn, G Rajchakit, J Thipcha, C Emharuethai, R Sriraman, Chee Peng LimChee Peng Lim, R Ramachandran
In practical applications, stochastic effects are normally viewed as the major sources that lead to the system’s unwilling behaviours when modelling real neural systems. As such, the research on network models with stochastic effects is significant. In view of this, in this paper, we analyse the issue of robust stability for a class of uncertain complex-valued stochastic neural networks (UCVSNNs) with time-varying delays. Based on the real-imaginary separate-type activation function, the original UCVSNN model is analysed using an equivalent representation consisting of two real-valued neural networks. By constructing the proper Lyapunov–Krasovskii functional and applying Jensen’s inequality, a number of sufficient conditions can be derived by utilizing It o ^ ’s formula, the homeomorphism principle, the linear matrix inequality, and other analytic techniques. As a result, new sufficient conditions to ensure robust, globally asymptotic stability in the mean square for the considered UCVSNN models are derived. Numerical simulations are presented to illustrate the merit of the obtained results.

History

Journal

Mathematics

Volume

8

Article number

ARTN 742

Pagination

1 - 19

Location

Basel, Switzerland

Open access

  • Yes

eISSN

2227-7390

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

5

Publisher

MDPI