期刊论文

Cao, Jinde;Huang, Chuangxia

英文

Mathematical Methods in the Applied Sciences

0170-4214

2020

43

10

6223-6253

10.1002/mma.6367

This article has been written with the joint financial support of the National Natural Science Foundation of China (Nos. 11971076 and 51839002), RUSA‐Phase 2.0 Grant No. F 24‐51/2014‐U, Policy (TN Multi‐Gen), Dept. of Edn. Govt. of India, UGC‐SAP (DRS‐I) Grant No. F.510/8/DRS‐I/2016(SAP‐I), and DST (FIST ‐ level I) 657876570 Grant No. SR/FIST/MS‐I/2018/17. J. Alzabut would like to thank Prince Sultan University for supporting this work through research group Nonlinear Analysis Methods in Applied Mathematics (NAMAM) group number RG‐DES‐2017‐01‐17 and Thailand Research Grant FundRSA6280004. This article has been written with the joint financial support of the National Natural Science Foundation of China (Nos. 11971076 and 51839002), RUSA-Phase 2.0 Grant No. F 24-51/2014-U, Policy (TN Multi-Gen), Dept. of Edn. Govt. of India, UGC-SAP (DRS-I) Grant No. F.510/8/DRS-I/2016(SAP-I), and DST (FIST - level I) 657876570 Grant No. SR/FIST/MS-I/2018/17. J. Alzabut would like to thank Prince Sultan University for supporting this work through research group Nonlinear Analysis Methods in Applied Mathematics (NAMAM) group number RG-DES-2017-01-17 and Thailand Research Grant FundRSA6280004.

本校为通讯机构

Fractional order quaternion-valued neural networks are a type of fractional order neural networks for which neuron state, synaptic connection strengths, and neuron activation functions are quaternion. This paper is dealing with the Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field. The fractional order quaternion-valued neural networks are separated into four real-valued systems forming an equivalent four real-valued fractional order neural networks, which decreases the compu...