The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control

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Date
2018
Volume
13
Issue
9
Journal
PLOS ONE
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San Francisco, California, US : PLOS
Abstract

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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Wang, W., Yu, X., Luo, X., Wang, L., Li, L., Kurths, J., et al. (2018). The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control (San Francisco, California, US : PLOS). San Francisco, California, US : PLOS. https://doi.org//10.1371/journal.pone.0204002
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CC BY 4.0 Unported