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

Nina Huo; Bing Li; Yongkun Li

doi:10.34768/amcs-2020-0007

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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: Nina Huo, Bing Li and Yongkun Li

Open Access

|Apr 2020

Abstract

A class of Clifford-valued high-order Hopfield neural networks (HHNNs) with state-dependent and leakage delays is considered. First, by using a continuation theorem of coincidence degree theory and the Wirtinger inequality, we obtain the existence of anti-periodic solutions of the networks considered. Then, by using the proof by contradiction, we obtain the global exponential stability of the anti-periodic solutions. Finally, two numerical examples are given to illustrate the feasibility of our results.

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Articles in this issue

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,

Related subjects:

© 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)