快速同步控制和用于加密-解密的应用程序与偶联的神经网络的间歇性随机干扰的加密-解密
Xianghui Zhou1, Jinde Cao2, Zhi-Hong Guan3
1School of Mathematics and Statistics, Anhui Normal University, Wuhu 241000, Anhui, China.
概括
这项研究引入了具有独特随机间歇性干扰的新型合神经网络. 该研究为这些网络开发了快速同步控制策略,证明了图像加密和解密的有效性.
科学领域:
- 复杂的系统复杂的系统.
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
背景情况:
- 现实世界的神经网络通常在受破坏的环境条件下运行.
- 现有的随机神经网络模型可能无法完全捕捉复杂的干扰模式.
- 开发强大的神经网络同步控制对于先进的应用程序至关重要.
研究的目的:
- 设计一种新类型的合神经网络,具有新的随机间歇性干扰.
- 研究快速同步控制策略,包括指数和预设时间同步.
- 将这些网络和控制方法应用于图像加密-解密.
主要方法:
- 开发了一种具有独特扰动机制的新类结合神经网络.
- 控制器的设计用于指数和预设时间同步使用可调的参数.
- 在同步条件下应用利亚普诺夫稳定原理,拉普拉斯矩阵和不等式技术.
- 使用驱动响应网络配置进行图像加密解密.
主要成果:
- 为设计的神经网络建立了快速同步条件.
- 通过数值示例证明了拟议的控制方案的有效性.
- 验证了驱动响应网络模型对图像加密解密的成功应用.
- 分析了控制因素对同步速度的影响.
结论:
- 拟议的带有随机间歇性干扰的结合神经网络准确模拟受干扰的环境.
- 开发的快速同步控制策略对于实现指数和预设时间同步都是有效的.
- 图像加密解密应用程序强调了这些先进的神经网络模型的实际实用性.
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