DocumentCode :
232019
Title :
Boundedness and exponential stability for high-order neural networks with time-varying delay
Author :
Ren Fengli ; Zhao Hongyong
Author_Institution :
Dept. of Math., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
fYear :
2014
fDate :
28-30 July 2014
Firstpage :
5031
Lastpage :
5036
Abstract :
In this paper, by using the Young inequality technique and introducing many real parameters, some criteria are established for the boundedness and exponential stability of a class of high-order neural networks with time-varying delay. The upper bound of the solutions are also estimated, which is seldom considered before. The results obtained in this paper generalize and improve the existing ones since they can be considered as a special case of our ones as a lower-order case. Illustrative example is also given in the end of this paper to show the effectiveness of our results.
Keywords :
asymptotic stability; delays; neural nets; Young inequality technique; boundedness; exponential stability; high-order neural networks; time-varying delay; Biological neural networks; Control theory; Delays; Mathematical model; Neurons; Stability criteria; Boundedness; Dini derivative; Exponential stability; High-order neural networks; Time-varying delay;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
Type :
conf
DOI :
10.1109/ChiCC.2014.6895795
Filename :
6895795
Link To Document :
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