@article{wang_trade_2022,
	title = {Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks},
	volume = {148},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0893608022000041},
	doi = {10.1016/j.neunet.2022.01.004},
	abstract = {This paper concentrates on trade off analysis between fixed-time stabilization and energy consumption for a type of nonlinear neural networks (NNs). By constructing a compound switching controller and utilizing inequality techniques, a sufficient condition is proposed to ensure the fixed-time stabilization. Then, an estimate of the upper bound of the energy consumed by the controller in the control process is given. Furthermore, the quantitative analysis of the trade-off between the control time and energy consumption is studied. This article reveals that appropriate control parameters can balance the above two indicators to achieve an optimal control state. Finally, the presented theoretical results are verified by two numerical examples.},
	language = {en},
	urldate = {2022-02-05},
	journal = {Neural Networks},
	author = {Wang, Yuchun and Zhu, Song and Shao, Hu and Wang, Li and Wen, Shiping},
	month = apr,
	year = {2022},
	keywords = {/unread},
	pages = {66--73},
}

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