This study focused on the prescribed-time synchronization of inertial memristive neural networks (IMNNs) with time-varying delays. Based on existing prescribed-time control theory, a prescribed-time feedback controller suitable for IMNNs was designed by means of a time-varying scaling function. Moreover, the sufficient criteria for the prescribed-time synchronization (PTS) of IMNNs was derived using the non-reduced order method and the prescribed-time stability lemma. The effectiveness of our theoretical results was conclusively demonstrated through a numerical simulation.

This paper investigated novel predefined-time stability theorems for time-delayed fuzzy Cohen-Grossberg neural networks. A novel predefined-time stability lemma was introduced via a newly developed inequality-based analytical framework.The theoretical results demonstrated that, compared to existing stability criteria in the literature, is provided more precise estimation of settling time boundaries, but also effectively reduced conservatism. To validate the effectiveness of the proposed lemma, the stability theorem was applied to the synchronization control problem of fuzzy Cohen-Grossberg neural networks (FCGNNs).To address this, an adaptive control strategy was proposed, employing a discontinuous state-feedback approach for the response neural network. Rigorous algebraic criteria was established to ensure synchronization within the specified time frame, in line with prior discussions. The effectiveness of the proposed synchronization method was empirically verified through numerical case studies.