Stop and go adaptive strategy for synchronization of delayed memristive recurrent neural networks with unknown synaptic weights

DOI10.1016/j.jfranklin.2017.05.011zbMath1367.93303OpenAlexW2615927940MaRDI QIDQ2011882

Huaguang Zhang, Sanbo Ding, Haisha Niu, Zhan-Shan Wang

Publication date: 27 July 2017

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2017.05.011

zbMATH Keywords

drive-response synchronizationmemristive recurrent neural networks (MRNNs)stop and go adaptive strategy

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Control/observation systems with incomplete information (93C41) Adaptive control/observation systems (93C40)

Related Items (8)

Delay-range-dependent ℋ^∞ synchronization approaches for time-delay chaotic systems ⋮ Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays ⋮ Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control ⋮ Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control ⋮ Event-triggered adaptive control for delayed memristive neural networks with unknown parameters and external disturbances ⋮ Analysis of adaptive synchronization for stochastic neutral-type memristive neural networks with mixed time-varying delays ⋮ Global synchronization in nonlinearly coupled delayed memristor-based neural networks with excitatory and inhibitory connections ⋮ Finite/fixed-time synchronization control of coupled memristive neural networks

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