Predefined-Time Synchronization of Stochastic Memristor-Based Bidirectional Associative Memory Neural Networks With Time-Varying Delays
Qingjie Wang, Hui Zhao, Aidi Liu, Lixiang Li, Sijie Niu, Chuan Chen
Abstract
In this article, we consider the predefined-time synchronization problem of stochastic memristor-based bidirectional associative memory neural networks (MBAMNNs). First, considering the influence of stochastic disturbance, we propose two new predefined-time theorems. Second, combined with the predefined-time theorem proposed in this article, we design a feedback controller to realize the predefined-time synchronization of MBAMNNs. Then, the sufficient conditions for the predefined-time synchronization of MBAMNNs are obtained based on Ito’s formula and Lyapunov theorem. Finally, the effectiveness of the theorems is verified by numerical simulations.
Topics & Concepts
Computer scienceMemristorBidirectional associative memorySynchronization (alternating current)Artificial neural networkContent-addressable memoryAssociative propertyContent-addressable storageArtificial intelligenceComputer networkElectronic engineeringChannel (broadcasting)Pure mathematicsMathematicsEngineeringAdvanced Memory and Neural ComputingNeural Networks Stability and SynchronizationNeural Networks and Applications