Event-Triggered Unified Performance State Estimation for Neural Networks with Time-Varying Delays

This paper tackles the problem of event-triggered unified performance state estimation in neural networks with time-varying delays. A novel event-triggered methodology is introduced, aiming to balance the performance of the state estimator and the network's communication bandwidth. The proposed method leverages a triggered-parameter-dependent integral inequality with matrices that consider the event-triggered mechanism, capturing the interplay between the time-varying delay and system states. This innovative approach guarantees the asymptotic stability of the estimation error system, thereby meeting the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H$</tex> ∞ performance criterion. The efficacy of the proposed condition is demonstrated by a numerical example.