This paper studies the stability properties of a class of Hopfield-type neural networks involving conformable derivatives and piecewise constant arguments. By constructing an associated discrete-time formulation, the continuous system is expressed in a form that is more suitable for analysis. A Lyapunov-based approach is then developed to investigate the asymptotic and exponential stability of the equilibrium point of the resulting discrete system. The analysis provides conditions that depend on the system parameters and the conformable derivative order, offering insight into the convergence behavior of solutions. The proposed approach treats the discrete formulation as an analytical tool for studying the original model. A numerical example is included to illustrate the theoretical results.
Oztepe et al. (Sun,) studied this question.