Title
Synchronization for Quantized Semi-Markov Switching Neural Networks in a Finite Time
Abstract
Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.
Year
DOI
Venue
2021
10.1109/TNNLS.2020.2984040
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Finite-time controller,logarithmic quantizer,weak infinitesimal operator
Journal
32
Issue
ISSN
Citations 
3
2162-237X
1
PageRank 
References 
Authors
0.35
30
5
Name
Order
Citations
PageRank
Wenhai Qi113510.51
Ju H. Park25878330.37
Guangdeng Zong376753.03
Jinde Cao411399733.03
Jun Cheng553643.22