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Anti–Periodic Solutions for Clifford–Valued High–Order Hopfield Neural Networks with State–Dependent and Leakage Delays Cover

Anti–Periodic Solutions for Clifford–Valued High–Order Hopfield Neural Networks with State–Dependent and Leakage Delays

International Journal of Applied Mathematics and Computer Science
Volume 30 (2020): Issue 1 (March 2020)
By: ,   and    
Open Access
|Apr 2020
Authors

Authors

Nina Huo

Department of Mathematics, Yunnan University, Yunnan, China

Bing Li

School of Mathematics and Computer Science, Yunnan Nationalities University, Yunnan, China

Yongkun Li

yklie@ynu.edu.cn

Department of Mathematics, Yunnan University, Yunnan, China
DOI: https://doi.org/10.34768/amcs-2020-0007 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 83 - 98
Submitted on: Mar 14, 2019
Accepted on: Oct 18, 2019
Published on: Apr 3, 2020
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Clifford-valued high-order Hopfield neural network,
anti-periodic solution,
coincidence degree,
time-varying delay
Related subjects:
Mathematics,
Applied mathematics

© 2020 Nina Huo, Bing Li, Yongkun Li, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 30 (2020): Issue 1 (March 2020)
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