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Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions.
The systems under consideration are subjected to time delays, multiplicative noises and randomly occurring missing measurements.
The phenomena of ROQ and missing measurements are governed by a Bernoulli distributed stochastic sequence.
The phenomenon of missing measurements is described by a Bernoulli process.
The uncertainties of the systems include multiplicative noises, missing measurements, and uncertain noise variances.
The gap filling technique used to patch the missing measurements is also presented.
The considered incomplete information includes both the sensor saturations and the missing measurements.
In this paper, a sampled-data H∞ fuzzy filtering problem is considered for nonlinear systems with missing measurements.
More precisely, a stochastic variable satisfying the Bernoulli random distribution is introduced to describe the missing measurements.
Since the sensor measurements are not ideal in many applications, the phenomenon of missing measurements is considered for the deconvolution.
The missing measurements appear in a random way which is governed by missing rates obeying a certain probability distribution.
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