Non-fragile finite-time l2−l∞ state estimation for discrete-time neural networks with semi-Markovian switching and random sensor delays based on Abel lemma approach

This paper focuses the non-fragile state estimation problem for a class of discrete-time neural networks with semi-Markov switching and unreliable communication links in finite time l2−l∞ sense that are caused due to the randomly occurring sensor nonlinearity, randomly occurring time delays and packet dropouts. By employing semi-Markovian switching with time-varying transition rates, a broader class of dynamical systems than the traditional Markovian jump linear systems is described. Then, based on the Abel lemma approach on finite sum inequalities, a non-fragile state estimator is obtained to ensure that the resulting error system is mean-square stochastically finite-time stable with a prescribed l2−l∞ performance. Sufficient conditions for the gain of the state estimator are obtained through solving a set of linear matrix inequalities. Finally a numerical example is provided to substantiate the theoretical results.