# New conditions for stability of multiple delayed Cohen-Grossberg Neural Networks of neutral-type

## TL;DR

This paper presents new stability conditions for a type of neural network with multiple delays, ensuring stable behavior under specific constraints.

## Contribution

The paper introduces novel algebraic stability conditions for neutral-type Cohen-Grossberg neural networks with multiple delays.

## Key findings

- New stability conditions are derived using Lyapunov functionals and the Lyapunov theorem.
- The proposed conditions are formulated as algebraic equations for easy verification.
- A numerical example demonstrates the effectiveness of the new stability criteria.

## Abstract

In this research article, we essentially aim to examine the stability properties of a certain type of Cohen-Grossberg neural network. The analysed neural network involves multiple delay parameters. These delay parameters complicate the dynamical behaviour of the system, thereby increasing the risk of oscillations and chaotic behaviour, which adversely affect system stability. However, under specific system parameter constraints, the stability of the system can be ensured. In our study, we developed new adequate stability conditions that guarantee global asymptotic stability for neutral-type Cohen-Grossberg artificial neural networks with multiple delays. These conditions, which can serve as an alternative to the results in the literature, are derived by utilizing suitable Lyapunov functionals and the Lyapunov theorem. The proposed stability conditions are formulated as algebraic equations. Within this context, our proposed stability conditions can be easily examined by using some mathematical methods and software tools. By carrying out a detailed analysis of an instructive numerical example, the results obtained in this article are also shown to establish alternative stability criteria to the corresponding stability conditions given in the past literature.