Nonfragile State Estimation of Quantized Complex Networks With Switching Topologies

This paper considers the nonfragile H_\infty estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled si...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-10, Vol.29 (10), p.5111-5121
Main Authors: Wu, Zheng-Guang, Xu, Zhaowen, Shi, Peng, Chen, Michael Z. Q., Su, Hongye
Format: Article
Language:English
Subjects:
Bandwidths
Complex networks
Complexity theory
Counters
Estimation
Estimators
Matrix methods
Measurement
nonfragile estimation
Parameter estimation
Quantization (signal)
quantization effects
Signal processing
State estimation
Switches
Switching theory
switching topologies
Topology
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Summary:This paper considers the nonfragile H_\infty estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties. The concept of nonfragility is introduced by inserting randomly occurred uncertainties into the estimator parameters to cope with the unavoidable small gain variations emerging from the implementations of estimators. Both the quantizers and the estimators have several operation modes depending on the switching signal of the underlying network structure. A sufficient condition is provided via a linear matrix inequality approach to ensure the estimation error dynamic to be stochastically stable in the absence of external disturbances, and the H_\infty performance with a prescribed index is also satisfied. Finally, a numerical example is presented to clarify the validity of the proposed method.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2018.2790982