However, if considering the intermittent sensor failures, the phenomenon of missing measurements occurs.
The proposed randomly occurring phenomena include both probabilistic missing measurements and randomly occurring coupling delays which are described by two random variable sequences satisfying individual probability distributions, respectively.
The systems under consideration are subjected to time delays, multiplicative noises and randomly occurring missing measurements.
In this paper, the problem of fault tolerant H∞ control for a class of networked control systems with randomly occurring missing measurements is investigated.
A random variable sequence following the Bernoulli distribution with time-varying probability is employed to character the missing measurements which are assumed to occur in a random way.
By employing a stochastic Kronecker delta function, the phenomena of the incomplete measurements are characterized in a unified framework which contain the randomly occurring signal quantization and the missing measurements.
The purpose of the addressed problem is to design a full-order filter such that, in the simultaneous presence of distributed delays, randomly occurring nonlinearities, sensor saturation and missing measurements, the filtering dynamic system is guaranteed to be exponentially mean-square stable, and the H∞ filtering performance index is achieved.
This paper is concerned with the H∞ state estimation problem for a class of discrete-time neural networks with time-varying delays, randomly occurring quantizations (ROQs) as well as missing measurements.
This paper is concerned with the H∞ filtering problem for a class of networked systems subject to randomly occurring distributed state delays, nonlinearities, sensor saturation as well as missing measurements via unreliable communication channels.
Only 25 days have missing measurements.
