The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control
Author(s) -
Weiping Wang,
Xin Yu,
Xiong Luo,
Long Wang,
Lixiang Li,
Jürgen Kurths,
Wenbing Zhao,
Jiuhong Xiao
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0204002
Subject(s) - exponential stability , control theory (sociology) , lyapunov function , bidirectional associative memory , artificial neural network , equilibrium point , content addressable memory , associative property , leakage (economics) , computer science , stability (learning theory) , lyapunov stability , mathematics , control (management) , artificial intelligence , differential equation , nonlinear system , machine learning , mathematical analysis , physics , quantum mechanics , pure mathematics , economics , macroeconomics
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.
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