Symmetrical Impulsive Inertial Neural Networks with Unpredictable and Poisson-Stable Oscillations

This paper explores the novel concept of discontinuous unpredictable and Poisson-stable motions within impulsive inertial neural networks. The primary focus is on a specific network architecture where impulses mimic structure original model, that is, continuous discrete parts are symmetrical. unique modeling decision aligns with real-world behavior systems, voltage typically remains smooth but may exhibit sudden changes due to various factors such as switches, loads, or faults. introduces representation these abrupt transitions derivatives, providing more accurate depiction scenarios. Thus, research exceptional in its generality. To study Poisson stability, method included intervals extended for functions B-topology. theoretical findings substantiated numerical examples, demonstrating practical feasibility proposed model.