Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties
Yihao Wang, Yanli Huang, Erfu Yang
Abstract
A multi‐weighted coupled neural networks (MWCNNs) model with event‐triggered communication is studied here. On the one hand, the passivity of the presented network model is studied by utilising Lyapunov stability theory and some inequality techniques, and a synchronisation criterion based on the obtained output‐strict passivity condition of MWCNNs with event‐triggered communication is derived. On the other hand, some robust passivity and robust synchronisation criteria based on output‐strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output‐strict passivity and robust synchronisation results.
Topics & Concepts
PassivityControl theory (sociology)Artificial neural networkComputer scienceStability (learning theory)Synchronization (alternating current)Lyapunov functionEvent (particle physics)Control (management)EngineeringArtificial intelligenceNonlinear systemTelecommunicationsPhysicsQuantum mechanicsMachine learningElectrical engineeringChannel (broadcasting)Neural Networks Stability and SynchronizationDistributed Control Multi-Agent SystemsNonlinear Dynamics and Pattern Formation