Open AccessInvariant Set and Periodicity for Delayed Neural Networks on Time Scales
Author(s) -
Liang-bo CHEN,
Zhenkun Huang,
Juan Chen
Publication year - 2017
Publication title -
destech transactions on engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2475-885X
DOI - 10.12783/dtetr/icca2016/5990
Subject(s) - invariant (physics) , mathematics , set (abstract data type) , exponential function , artificial neural network , discrete time and continuous time , pure mathematics , control theory (sociology) , computer science , mathematical analysis , artificial intelligence , statistics , control (management) , mathematical physics , programming language
In this paper, we investigate invariant set and periodicity of non-autonomous neural networks with time-varying delays on time scales. Based calculus of time scales, we apply M-matrix and inequality analysis techniques to construct invariant set and obtain existence of a globally exponential periodic solution in the invariant set. Our results are general and can include continuous or discrete ones.
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