MITTAG-LEFFLER STABILITY ANALYSIS OF TEMPERED FRACTIONAL NEURAL NETWORKS WITH SHORT MEMORY AND VARIABLE-ORDER

Autor:	Feng-Xia Zheng, Chuan-Yun Gu, Babak Shiri
Rok vydání:	2021
Předmět:	Stability conditions Artificial neural network Banach fixed-point theorem Applied Mathematics Modeling and Simulation Applied mathematics Order (group theory) Short-term memory Geometry and Topology Stability (probability) Variable (mathematics) Mathematics
Zdroj:	Fractals. 29
ISSN:	1793-6543 0218-348X
DOI:	10.1142/s0218348x21400296
Popis:	A class of tempered fractional neural networks is proposed in this paper. Stability conditions for tempered fractional neural networks are provided by using Banach fixed point theorem. Attractivity and Mittag-Leffler stability are given. In order to show the efficiency and convenience of the method used, tempered fractional neural networks with and without delay are discussed, respectively. Furthermore, short memory and variable-order tempered fractional neural networks are proposed under the global conditions. Finally, two numerical examples are used to demonstrate the theoretical results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5b5d8d9b426fe500ccd2e855b6cdb89b https://doi.org/10.1142/s0218348x21400296 Zobrazit plný text záznamu