English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper aims to investigate the global exponential stability of a class of inertial Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays. By constructing a modified delay-dependent Lyapunov-Krasovskii functional, delay-dependent criteria stated with simple algebraic inequalities are given in order to ensure the global exponential stability for the addressed neural network model. In sharp contrast to the existed reduced order method used to and delay-independent criteria derived for the neural networks with inertial terms, the model proposed and results established of this paper are more general and rigorous. Finally, numerical examples with simulations are presented to illustrate the main results.

Global exponential stability of inertial Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays