Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864213 | Neurocomputing | 2018 | 19 Pages |
Abstract
In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A stochastic Kronecker delta function is introduced to model such phenomena. The main work aims to design an asynchronous state estimator that assures the underlying error dynamics to be strictly (X,Y,Z)âγâdissipative. Based on the stochastic Kronecker delta function method and stochastic analysis technique, we establish some conditions for the sake of guaranteeing the existence of the desired state estimator. We finally explain the effectiveness of the proposed design approach by means of a numerical example.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yunzhe Men, Xia Huang, Zhen Wang, Hao Shen, Bo Chen,