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Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Jun2017, Vol. 28 Issue 6, p1473-1480. 8p. - Publication Year :
- 2017
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 28
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 123183866
- Full Text :
- https://doi.org/10.1109/TNNLS.2016.2532351