Finite-Time L_∞ Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

摘要

This paper, by proposing a sampled-data control scheme, we investigate the finite-time L_∞ performance state estimation of recurrent neural networks. By constructing a novel Lya-punov functional, new stability and stabilization conditions are derived. By utilizing integral inequality techniques, sufficient LMI conditions are derived to ensure the finite-time stability of considered neural networks. Furthermore, finite-time observer gain analysis of recurrent neural networks is set up to measure its disturbance tolerance capability in the fixed time interval. Numerical examples are given to verify the effectiveness of the proposed approach.