Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/52751
Title: Global exponential stability criteria for bidirectional associative memory neural networks with time-varying delays
Authors: J. Thipcha
P. Niamsup
Authors: J. Thipcha
P. Niamsup
Keywords: Mathematics
Issue Date: 28-Jun-2013
Abstract: The global exponential stability for bidirectional associative memory neural networks with time-varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov-Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential stability for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature. © 2013 J. Thipcha and P. Niamsup.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84879318346&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/52751
ISSN: 16870409
10853375
Appears in Collections:CMUL: Journal Articles

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