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 $H$ ∞ performance criterion. The efficacy of the proposed condition is demonstrated by a numerical example.

I. Introduction

Over the past few decades, the effective use of neural networks in areas such as signal processing, manipulator control, and pattern recognition has garnered significant interest [1]. Furthermore, due to constraints in information processing speed and hardware network parameters, time delays are an unavoidable aspect of practical implementation. Consequently, the study of delayed neural networks (DNN s) remains a vibrant area of research [2], [3].