Journal
NEURAL NETWORKS
Volume 71, Issue -, Pages 37-44Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2015.07.012
Keywords
Fractional-order; Memristor-based neural networks; Stability; Synchronization
Funding
- National Natural Science Fundation of China [50925727, 61403115, 51177035, 61272530, 11072059]
- Natural Science Foundation of Anhui Province [1508085QF120]
- National Defense Advanced Research Project [C1120110004]
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Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results. (C) 2015 Elsevier Ltd. All rights reserved.
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