EXPONENTIAL STABILITY OF HOPFIELD CONFORMABLE FRACTIONAL-ORDER POLYTOPIC NEURAL NETWORKS

Abstract

Due to many reasons such as linear approximation, external noises, modeling inaccuracies, measurement errors, and so on, uncertain disturbances are usually unavoidable in real dynamical systems. Convex polytopic uncertainties are one of a kind of these disturbances. In this paper, we consider the problem of fractional exponential stability for a class of Hopfield fractional-order neural networks (FONNs) subject to conformable derivative and convex polytopic uncertainties. By using the fractional Lyapunov functional method combined with some calculations on matrices, a new sufficient condition on fractional exponential stability for conformable FONNs is established via linear matrix inequalities (LMIs), which therefore can be efficiently solved in polynomial time by using the existing convex algorithms. The proposed result is quite general and improves those given in the literature since many factors such as conformable fractional derivative, convex polytopic uncertainties, exponential stability, are considered. A numerical example is provided to demonstrate the correctness of the theoretical results.