Finite-time stability analysis of fractional-order neural networks with delay

Finite-time stability analysis of fractional-order neural networks with delay. Yang, X., Song, Q., Liu, Y., & Zhao, Z. Neurocomputing, 152:19–26, March, 2015.
$Finite-time stability analysis of fractional-order neural networks with delay [link]$ Paper doi abstract bibtex

Stability analysis of fractional-order neural networks with delay is addressed in this paper. By using the contracting mapping principle, method of iteration and inequality techniques, a sufﬁcient condition is established to ensure the existence, uniqueness and ﬁnite-time stability of the equilibrium point of the proposed networks. Finally, based on the Predictor-Corrector Approach, two numerical examples are presented to illustrate the validity and feasibility of the obtained result.

@article{yang_finite-time_2015,
	title = {Finite-time stability analysis of fractional-order neural networks with delay},
	volume = {152},
	issn = {09252312},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0925231214015240},
	doi = {10.1016/j.neucom.2014.11.023},
	abstract = {Stability analysis of fractional-order neural networks with delay is addressed in this paper. By using the contracting mapping principle, method of iteration and inequality techniques, a sufﬁcient condition is established to ensure the existence, uniqueness and ﬁnite-time stability of the equilibrium point of the proposed networks. Finally, based on the Predictor-Corrector Approach, two numerical examples are presented to illustrate the validity and feasibility of the obtained result.},
	language = {en},
	urldate = {2023-03-31},
	journal = {Neurocomputing},
	author = {Yang, Xujun and Song, Qiankun and Liu, Yurong and Zhao, Zhenjiang},
	month = mar,
	year = {2015},
	keywords = {/unread},
	pages = {19--26},
}

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