A new Lyapunov functional for stability analysis of neutral-type Hopfield neural networks with multiple delays. (September 2020)

• Record Type: Journal Article
• Authors: Faydasicok, Ozlem
• Is Part Of: Neural networks. Volume 129(2020)
• Page Start: 288
• Page End: 297
• Publication Date: 2020-09
• Subjects: Neutral-type Hopfield systems -- Multiple delays -- Lyapunov theorems -- Stability analysis; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.06.013
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This research paper conducts an investigation into the stability issue for a more general class of neutral-type Hopfield neural networks that involves multiple time delays in the states of neurons and multiple neutral delays in the time derivatives of the states of neurons. By constructing a new proper Lyapunov functional, an alternative easily verifiable algebraic criterion for global asymptotic stability of this type of Hopfield neural systems is derived. This new stability condition is entirely independent of time and neutral delays. Two instructive examples are employed to indicate that the result obtained in this paper reveals a new set of sufficient stability criteria when it is compared with the previously reported stability results. Therefore, the proposed stability result enlarges the application domain of Hopfield neural systems of neutral types.