Delay-probability-dependent state estimation for neural networks with hybrid delays

Abstract

This dissertation studies the delay-probability-dependent H∞ state estimation issue of neural networks (NNs) with hybrid delays. First, more general system model and state estimator are established by considering discrete delay, distributed delay and probability distribution of time delays. Second, a innovative Lyapunov-Krasovskii functional (LKF) containing augmented non-integral and single-integral quadratic terms is put forward, which can inflect internal connections of multiple functional terms. Meanwhile, in order to handle the infinitesimal operators of LKF effectively, generalized free-weighting-matrix integral inequality (GFWMII) is chosen to cooperate with wirtinger-based inequality. As a consequence, less conservative criteria are obtained, which ensure that the considered system is asymptotically mean-square stable with a desired H∞ performance. Finally, two simulated examples are displayed to bring out the advantage of the achieved approach.