@article{Yang2023, 
author = {Chao Yang and Juntao Wu and Zhengyang Qiao},
title = {An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach},
year = {2023},
journal = {Electronic Research Archive},
volume = {31},
number = {5},
pages = {2428-2446},
keywords = {fixed-time stabilization, memristor, nonlinear coupling, indefinite derivative},
url = {https://www.sciopen.com/article/10.3934/era.2023123},
doi = {10.3934/era.2023123},
abstract = {In this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling. Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS) by designing a proper pinning controller, which randomly controls a small number of neuron nodes. Different from the traditional Lyapunov method, this paper uses the implementation method of indefinite derivative to deal with the non-autonomous neural network system with nonlinear coupling topology between different neurons. The system can obtain stabilization in a fixed time and requires fewer conditions. Moreover, the fixed stable setting time estimation of the system is given through a few conditions, which can eliminate the dependence on the initial value. Finally, we give two numerical examples to verify the correctness of our results.}
}