Journal
NEURAL NETWORKS
Volume 101, Issue -, Pages 33-46Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2018.01.015
Keywords
Memristor; Complex-valued neural networks; Discontinuous activation functions; Matrix inequalities; Delay-dependent stability
Funding
- Excellent Doctor Innovation Program of Xinjiang University [XJUBSCX-2016004]
- National Natural Science Foundation of People's Republic of China [61473244, 61563048, U1703262, 11402223]
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This paper considers the delay-dependent stability of memristive complex-valued neural networks (MCVNNs). A novel linear mapping function is presented to transform the complex-valued system into the real-valued system. Under such mapping function, both continuous-time and discrete-time MCVNNs are analyzed in this paper. Firstly, when activation functions are continuous but not Lipschitz continuous, an extended matrix inequality is proved to ensure the stability of continuous-time MCVNNs. Furthermore, if activation functions are discontinuous, a discontinuous adaptive controller is designed to acquire its stability by applying Lyapunov-Krasovskii functionals. Secondly, compared with techniques in continuous-time MCVNNs, the Halanay-type inequality and comparison principle are firstly used to exploit the dynamical behaviors of discrete-time MCVNNs. Finally, the effectiveness of theoretical results is illustrated through numerical examples. (c) 2018 Elsevier Ltd. All rights reserved.
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