Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations

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 st...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 291; pp. 207 - 214
Main Authors: Men, Yunzhe, Huang, Xia, Wang, Zhen, Shen, Hao, Chen, Bo
Format: Journal Article
Language:English
Published: Elsevier B.V 24.05.2018
Subjects:
Asynchronous dissipative state estimation
Jumping neural networks
Sensor saturations
Signal quantization
Sensor saturations
Jumping neural networks
Asynchronous dissipative state estimation
Signal quantization
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary: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)−γ−dissipative. 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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.02.071