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

Chuan-Yun Gu (), Feng-Xia Zheng and Babak Shiri ()
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Chuan-Yun Gu: School of Mathematics, Sichuan University of Arts and Science, Dazhou 635000, P. R. China
Feng-Xia Zheng: School of Mathematics, Sichuan University of Arts and Science, Dazhou 635000, P. R. China2Department of Mathematics, Sichuan University, Chengdu 610064, P. R. China
Babak Shiri: Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, P. R. China

FRACTALS (fractals), 2021, vol. 29, issue 08, 1-12

Abstract: 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.

Keywords: Mittag-Leffler Stability; Tempered Fractional Neural Networks; Short Memory; Variable-Order Tempered Fractional Neural Networks (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0218348X21400296

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