Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time-varying delays and randomly occurring sensor nonlinearity

Abstract

A finite-time non-fragile state estimation algorithm is discussed in this article for discrete delayed neural networks with sensor failures and randomly occurring sensor nonlinearity. First, by using augmented technology, such system is modeled as a kind of nonlinear stochastic singular delayed system. Then, a finite-time state estimator algorithm is provided to ensure that the singular error dynamic is regular, causal and stochastic finite-time stable. Moreover, the states and sensor failures can be estimated simultaneously. Next, in order to avoid the affection of estimator’s parameter perturbation, a finite-time non-fragile state estimation algorithm is given, and a simulation result demonstrates the usefulness of the proposed approach.