在图像加密应用程序下的基于协议的随机跳跃惯性神经网络同步
IEEE transactions on neural networks and learning systems
|August 10, 2023
概括
本研究介绍了一种适应性事件驱动的协议,用于同步惯性神经网络 (INN) 与半马科维亚跳跃参数. 该方法节省了带宽,并确保了像图像加密这样的应用程序的系统同步.
科学领域:
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
- 信息安全 信息安全
背景情况:
- 惯性神经网络 (INN) 是复杂的系统,容易发生突然的参数变化.
- 随机的半马科维亚跳跃过程模拟了这些突然的环境或系统变化.
- 有效的同步协议对于节省网络带宽和避免Zeno现象至关重要.
研究的目的:
- 开发一个适应性事件驱动协议 (AEDP) 来同步半马科维亚跳跃INNs (S-MJINNs).
- 设计一个适应性事件驱动控制器,确保驱动和响应系统之间的同步.
- 在数值模拟和图像加密中证明拟议的同步方法的有效性.
主要方法:
- 为S-MJINNs量身定制的适应性事件驱动协议 (AEDP) 的开发.
- 使用Lyapunov功能,积分不等式和自由权重矩阵构建一个自适应式事件驱动控制器.
- 在随机半马科维亚跳跃条件下对同步标准的分析.
主要成果:
- 在S-MJINNs中实现同步的新标准得到了推导.
- 拟议的AEDP有效地减少了数据传输,并防止了Zeno现象.
- 成功演示了同步及其在图像加密中的应用.
结论:
- 开发的自适应性事件驱动控制器确保了INN与随机半马科维亚跳跃参数的稳健同步.
- 拟议的协议为带宽有限的场景提供了有效的解决方案.
- 该方法已被验证用于实际应用,包括安全的图像加密.
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