Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76342
Title: Quantized iterative learning consensus tracking of digital networks with limited information communication
Authors: Xiong, WJ
Yu, XH
Chen, Y 
Gao, J
Keywords: Digital networks
Limited information communication
Quantized iterative learning
Time-varying topologies
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2017, v. 28, no. 6, p. 1473-1480 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.
URI: http://hdl.handle.net/10397/76342
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2016.2532351
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