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离散时间分数模糊神经网络的同步通过量化控制延迟通过量化控制
Jikai Yang1, Hong-Li Li2, Long Zhang1
1College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
ISA transactions
|July 14, 2023
概括
这项研究解决了使用量子化控制的离散时间分数模糊神经网络 (DFFNNs) 的同步问题,增强了图像加密安全性. 新的不平等和方法确保了解决方案的存在和网络同步,证明了实际适用性.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 图像加密 图像加密
- 分数微积分的计算.
背景情况:
- 同步对于神经网络应用程序至关重要.
- 离散时间分数模糊神经网络 (DFFNNs) 由于延迟和分数动态,存在独特的挑战.
- 现有的同步方法可能缺乏实用性或效率.
研究的目的:
- 解决与延迟的离散时间分数模糊神经网络 (DFFNNs) 中的同步问题.
- 将同步技术应用于图像加密.
- 开发一个量化控制策略,以提高资源效率.
主要方法:
- 证明了一个新的分数级h差异不等式.
- 使用压缩映射定理和数学归纳.
- 设计一个资源高效的量子化控制器.
主要成果:
- 为DFFNN解决方案的存在和独特性建立了两个足够的条件.
- 为 DFFNNs.derived 推导了几个非保守的同步标准.
- 通过示例证明了拟议方法的有效性和实用性.
结论:
- 提出的量子化控制方法有效地解决了DFFNNs的同步问题.
- 形成的分数级不等式增强了莱普诺夫方法的灵活性.
- 同步技术成功应用于图像加密,展示了实际的实用性.
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