Finite-time bipartite lag synchronization of coupled memristive neural networks with uncertain disturbances via hybrid control

Abstract

This article is concerned about the finite-time bipartite lag synchronization of coupled memristive neural networks with time-varying delay and uncertain disturbances. To deal with the same and the different transmission lags in practical applications, some appropriate control strategies including two different phases are designed. Compared with some existing literatures of this subject, the restriction of the switching value is relaxed. Then, with the help of stability theory, matrix theory and algebra inequality theory, some Lyapunov-based synchronization criteria are derived. In addition, the settling time is given as a function of the initial values. Finally, the efficacy and accuracy of the suggested theoretical results are demonstrated through numerical examples and their simulations.

Global Dissipative Examination of Delayed Memristive Inertial Neural Networks with Uncertain Parameters

“…As an example of practical, the difficult diagnosis in the ICU due to the uncertain or missed diagnosis was investigated in [8] which impacts the importance. Stability of the memristor based delayed fractional-order Cohen-Grosberg NNs with time delays and uncertainities are discussed in [9]. The sufficient synchronization criteria of the fixed time stability on uncertain neural networks via Lyapunov stability theorem was addressed in [10].…”