Coexistence and local Mittag-Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions

doi:10.1088/1674-1056/28/4/040701

Abstract

In this paper, coexistence and local Mittag-Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions are addressed. Because of the discontinuity of the activation function, Filippov solution of the neural network is defined. Based on Brouwer's fixed point theorem and definition of Mittag-Leffler stability, sufficient criteria are established to ensure the existence of (2k+3)ⁿ (k ≥ 1) equilibrium points, among which (k+2)ⁿ equilibrium points are locally Mittag-Leffler stable. Compared with the existing results, the derived results cover local Mittag-Leffler stability of both fractional-order and integral-order recurrent neural networks. Meanwhile discontinuous networks might have higher storage capacity than the continuous ones. Two numerical examples are elaborated to substantiate the effective of the theoretical results.

Keywords: fractional-order recurrent neural network local Mittag-Leffler stability discontinuous activation function

Received: 06 September 2018
Revised: 03 December 2018
Accepted manuscript online:

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	02.30.Ks	(Delay and functional equations)

Fund:

Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LY18F030023, LY17F030016, and LY18F020028) and the National Natural Science Foundation of China (Grant Nos. 61503338, 61502422, and 61773348).

Corresponding Authors: Yu-Jiao Huang
E-mail: hyj0507@zjut.edu.cn

Cite this article:

Yu-Jiao Huang(黄玉娇), Shi-Jun Chen(陈时俊), Xu-Hua Yang(杨旭华), Jie Xiao(肖杰) Coexistence and local Mittag-Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions 2019 Chin. Phys. B 28 040701

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Coexistence and local Mittag-Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions

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