Fast fixed-/preassigned-time synchronization of Clifford-valued neural networks for medical image encryption

IF 5.5 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neurocomputing Pub Date : 2025-07-12 DOI:10.1016/j.neucom.2025.130984

Yanlin Zhang , Kit Ian Kou , Yanhui Zhang , Lizhi Liu

{"title":"Fast fixed-/preassigned-time synchronization of Clifford-valued neural networks for medical image encryption","authors":"Yanlin Zhang , Kit Ian Kou , Yanhui Zhang , Lizhi Liu","doi":"10.1016/j.neucom.2025.130984","DOIUrl":null,"url":null,"abstract":"<div><div>This paper aims to investigate the fixed-time (FXT) and preassigned-time (PAT) synchronization for Clifford-valued neural networks (CFVNNs) with mixed delays by improving a novel FXT stability theorem and using non-decomposing two-step method. First of all, a novel FXT stability theorem has been derived. Its time estimation formula and settling time are simpler and accurate compared to existing stability theorem. Then, based on this novel FXT stability theorem, the FXT synchronization of the CFVNNs is obtained by designing sample nonlinear controller and Lyapunov function and seeking the settling time. As a special case, the PAT synchronization of CFVNNs is investigated, in which the estimation of settling time is independent of any initial conditions of neural networks and any parameters of the designed controllers. Lastly, numerical examples demonstrate the effectiveness and superiority of the derived theoretical results. The research also extends to the practical domain, evaluating the impact of CFVNNs and the designed controllers on medical image encryption.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"651 ","pages":"Article 130984"},"PeriodicalIF":5.5000,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092523122501656X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

This paper aims to investigate the fixed-time (FXT) and preassigned-time (PAT) synchronization for Clifford-valued neural networks (CFVNNs) with mixed delays by improving a novel FXT stability theorem and using non-decomposing two-step method. First of all, a novel FXT stability theorem has been derived. Its time estimation formula and settling time are simpler and accurate compared to existing stability theorem. Then, based on this novel FXT stability theorem, the FXT synchronization of the CFVNNs is obtained by designing sample nonlinear controller and Lyapunov function and seeking the settling time. As a special case, the PAT synchronization of CFVNNs is investigated, in which the estimation of settling time is independent of any initial conditions of neural networks and any parameters of the designed controllers. Lastly, numerical examples demonstrate the effectiveness and superiority of the derived theoretical results. The research also extends to the practical domain, evaluating the impact of CFVNNs and the designed controllers on medical image encryption.

查看原文本刊更多论文

用于医学图像加密的clifford值神经网络快速固定/预分配时间同步

本文通过改进一个新的FXT稳定性定理，并采用非分解两步方法，研究了混合时滞Clifford-valued neural networks （CFVNNs）的固定时间（FXT）和预分配时间（PAT）同步问题。首先，导出了一个新的FXT稳定性定理。与现有的稳定性定理相比，其时间估计公式和稳定时间更简单准确。然后，基于这一新颖的FXT稳定性定理，通过设计样本非线性控制器和Lyapunov函数并求其稳定时间，实现了cfvnn的FXT同步。作为一种特殊情况，研究了cfvnn的PAT同步，其中沉降时间的估计与神经网络的任何初始条件和所设计控制器的任何参数无关。最后，通过数值算例验证了所得理论结果的有效性和优越性。研究还扩展到实际领域，评估了cfvnn和设计的控制器对医学图像加密的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Neurocomputing 工程技术-计算机：人工智能

CiteScore

13.10

自引率

10.00%

发文量

1382

审稿时长

70 days

期刊介绍： Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.