Work

Global attractivity criteria for a discrete-time Hopfield neural network model with unbounded delays via singular M—matrices

Year: 2025

Type: article

Abstract: In this work, we establish two global attractivity criteria for a multidimensional discrete-time non-autonomous Hopfield neural network model with infinite delays and delays in the leakage terms. The first criterion, which applies when the activation functions are bounded, is based on M -matrices that are not necessarily invertible. The second criterion, relevant for unbounded activation functions, requires that a related singular M -matrix be irreducible. We contrast our findings with existing results in the literature and present numerical simulations to illustrate the novelty of the proposed criteria.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131311

Authors: José J. Oliveira, Ana Sofia Teixeira

Institutions: Centro de Matemática, University of Minho

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funders: Universidade do Minho, Fundação para a Ciência e a Tecnologia, Centro de Matemática, Universidade do Minho