A Lyapunov-Based Analysis on the Almost Periodicity of Impulsive Conformable Reaction–Diffusion Neural Networks with Distributed Delays

Abstract

The focus of this research is the qualitative behavior of a reaction-diffusion neural network with distributed delays and conformable derivatives under impulsive perturbations. In particular, the almost periodic behavior of the proposed model is studied using a Lyapunov-based approach. By constructing an appropriate Lyapunov-type function, criteria that guarantee the existence and uniqueness of an almost periodic state are provided. The established criteria extend a few existing results on the almost periodicity of conformable models and contribute to the development of the field. In addition, the notion of global conformable exponential stability is introduced and analyzed for the developed model. A suitable example is discussed.