New Results on Stability of Delayed Cohen-Grossberg Neural Networks of Neutral Type


COMPLEXITY, vol.2020, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2020
  • Publication Date: 2020
  • Doi Number: 10.1155/2020/1973548
  • Journal Name: COMPLEXITY
  • Journal Indexes: Science Citation Index Expanded, Scopus, Applied Science & Technology Source, BIOSIS, Compendex, Computer & Applied Sciences, INSPEC, zbMATH, Directory of Open Access Journals


This research work conducts an investigation of the stability issues of neutral-type Cohen-Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. By setting a new generalized appropriate Lyapunov functional candidate, some novel sufficient conditions are proposed for global asymptotic stability for the considered neural networks of neutral type. This paper exploits some basic properties of matrices in the derivation of the results that establish a set of algebraic mathematical relationships between network parameters of this neural system. A key feature of the obtained stability criteria is to be independent from time and neutral delays. Therefore, the derived results can be easily tested. Moreover, a constructive numerical example is studied to check the verification of presented global stability conditions.