H_∞ state estimation for discrete-time switching neural networks with persistent dwell-time switching regularities

This paper focuses on the state estimation problem for a class of discrete-time switching neural networks with persistent dwell time (PDT) switching regularities and mode-dependent time-varying delays in H∞ sense. The considered switching regularity is more general that extends the frequently studied dwell-time (DT) and average dwell-time (ADT) switching. The random packet dropouts, which are governed by a Bernoulli distributed white sequence, are considered to exist together for the estimator design of underlying switching neural networks. The desired mode-dependent estimators are designed such that the resulting estimation error system is exponentially mean-square stable and achieves a prescribed H∞ level of disturbance attenuation. Finally, the effectiveness and the superiority of the developed results are demonstrated through a class of synthetic oscillatory networks.