# The existence and global exponential stability of almost periodic solutions for neutral type CNNs on time scales

## TL;DR

This paper establishes conditions for the existence and stability of almost periodic solutions in neutral type competitive neural networks on time scales, unifying continuous and discrete cases.

## Contribution

It introduces new sufficient conditions for stability that are independent of the time scale's graininess, covering both continuous and discrete neural networks.

## Key findings

- Existence of almost periodic solutions under new conditions
- Global exponential stability demonstrated for the solutions
- Unified results applicable to continuous and discrete time networks

## Abstract

In this paper, a class of neutral type competitive neural networks with mixed time-varying delays and leakage delays on time scales is proposed. Based on the exponential dichotomy of linear dynamic equations on time scales, Banach's fixed point theorem and the theory of calculus on time scales, some sufficient conditions that are independent of the backwards graininess function of the time scale are obtained for the existence and global exponential stability of almost periodic solutions for this class of neural networks. The obtained results are completely new and indicate that both the continuous time and the discrete time cases of the networks share the same dynamical behavior. Finally, an examples is given to show the effectiveness of the obtained results.