The primary objective of this work is to establish the existence, uniqueness, and exponential stability of piecewise weighted pseudo–almost automorphic solutions for impulsive high-order Hopfield neural networks formulated within Clifford algebras. Using the Banach fixed-point principle together with a suitably adapted Gronwall–Bellman inequality, we derive novel and verifiable sufficient conditions that ensure these qualitative properties. The main contributions are as follows: (i) this study is the first to analyze weighted pseudo–almost automorphic (WPAA) dynamics for impulsive high-order Hopfield neural networks directly in the Clifford algebra setting, without reducing the model t... More >

In this paper, we study the problem of predefined-time synchronization for distinct-order fractional delayed Cohen-Grossberg neural networks. Fractional-order models are known for their ability to capture memory effects and complex dynamics more accurately than classical integer-order systems. In particular, allowing distinct-order in the drive and response systems provides additional flexibility in modeling. To achieve synchronization, we propose two control strategies that provide sufficient conditions for predefined-time synchronization of the addressed model. These strategies are based on the construction of an appropriate Lyapunov function and the use of fractional calculus properties.... More >