指数同步和加密技术的进展:具有双面系数的四元数值人工神经网络

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chenyang Li, Kit Ian Kou, Yanlin Zhang, Yang Liu
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引用次数: 0

摘要

本文介绍了指数同步和加密技术的前沿进展,重点是包含双面系数的四元数值人工神经网络(QVANN)。该研究引入了一种新方法,利用 Cayley-Dickson 表示法简化 QVANN 固有的复杂方程,从而利用复数特性提高计算效率。该研究利用 Lyapunov 定理设计了一个弹性控制系统,通过巧妙地调节 Lyapunov 函数及其导数,展示了其指数同步性。这种管理确保了网络的稳定性和同步性,这对各种应用中的可靠性能至关重要。为了证实理论框架,本文进行了广泛的数值模拟,为所提出的设计和证明提供了实证支持。此外,论文还探讨了 QVANNs 在彩色图像加密和解密中的实际应用,展示了该网络高效处理复杂数据处理任务的能力。这项研究成果不仅为复杂人工神经网络的发展做出了重大贡献,而且为进一步探索具有不同延迟类型的系统铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancements in exponential synchronization and encryption techniques: Quaternion-Valued Artificial Neural Networks with two-sided coefficients.

This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued Artificial Neural Networks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach that harnesses the Cayley-Dickson representation method to simplify the complex equations inherent in QVANNs, thereby enhancing computational efficiency by exploiting complex number properties. The study employs the Lyapunov theorem to craft a resilient control system, showcasing its exponential synchronization by skillfully regulating the Lyapunov function and its derivatives. This management ensures the stability and synchronization of the network, which is crucial for reliable performance in various applications. Extensive numerical simulations are conducted to substantiate the theoretical framework, providing empirical evidence supporting the presented design and proofs. Furthermore, the paper explores the practical application of QVANNs in the encryption and decryption of color images, showcasing the network's capability to handle complex data processing tasks efficiently. The findings of this research not only contribute significantly to the development of complex artificial neural networks but pave the way for further exploration into systems with diverse delay types.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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