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Anti-periodic solutions of Clifford-valued fuzzy cellular neural networks with delays

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Abstract

This paper discusses a class of delayed Clifford-valued fuzzy cellular neural networks. First, although the multiplication of Clifford algebras does not satisfy the commutativity, without separating the Clifford-valued systems into real-valued systems. Second, we can obtain several sufficient conditions for the existence of anti-periodic solutions for Clifford-valued fuzzy cellular neural networks by using the non-decomposition method, and Schauder fixed point theorem. Third, we can obtain the global exponential stability of anti-periodic solutions for Clifford-valued fuzzy cellular neural networks by using the the proof by contradiction. Finally, we give one example to illustrate the feasibility and effectiveness of the main results.

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Funding

This research is supported by the Science Research Fund of Education Department of Yunnan Province of China [Grant Number 2018JS517] and the Mathematics and Applied Mathematics teaching team of Puer University 2020JXTD018.

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Authors and Affiliations

  1. School of Information, Yunnan Communications Vocational and Technical College, 2056 Wanqing Road, Chenggong University Town, Kunming, 650500, Yunnan, China

    Jin Gao

  2. School of Mathematics and Statistics, Southwest University, 2 Tiansheng Road, Beibei District, Chongqing, 400715, China

    Lihua Dai

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  1. Jin Gao
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  2. Lihua Dai
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Correspondence to Jin Gao.

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Communicated by Graçaliz Pereira Dimuro.

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Gao, J., Dai, L. Anti-periodic solutions of Clifford-valued fuzzy cellular neural networks with delays. Comp. Appl. Math. 41, 336 (2022). https://doi.org/10.1007/s40314-022-02051-6

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  • DOI: https://doi.org/10.1007/s40314-022-02051-6

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