基于探测器的边界同步控制隐藏的马尔科夫跳跃反应-扩散神经网络
Lin Sun1, Hailong Huang2, Yan Peng3
1School of Electrical and Information Engineering Tianjin University, Tianjin 300072, China.
概括
这项研究引入了一种基于探测器的新型边界控制,用于隐藏的马尔科夫跳跃反应-扩散神经网络,增强被动同步. 该方法减少了控制器的复杂性和成本,同时确保了系统的稳定性和性能.
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
- 随机系统 随机系统 随机系统
背景情况:
- 反应扩散神经网络表现出复杂的动态和参数变化.
- 隐藏的马尔科夫模型用于表示系统参数和结构的突然变化.
- 传统的同步方法通常需要完全状态的可观测性,这并不总是可行的.
研究的目的:
- 为连续时间隐藏的马尔科夫跳跃反应-扩散神经网络开发一种被动同步控制策略.
- 用基于探测器的方法解决马尔科夫跳跃系统中不可观测状态的挑战.
- 设计一个具有成本效益的边界控制方法,减少所需控制器的数量.
主要方法:
- 使用隐藏的马尔科夫跳跃模型,结合隐藏和观察到的状态.
- 使用探测器来估计隐藏的状态,放松了完全可观测性的假设.
- 一个模式依赖的边界同步控制器是在诺曼边界条件下设计的.
- 凸优化问题被解决以获得稳定性和被动性能的条件.
主要成果:
- 足够的条件来保证系统稳定性和预期的被动性能的衍生.
- 建议的边界控制器被证明比全域控制器更经济.
- 通过解决凸凸的优化问题来获得控制器收益.
- 通过比较的例子来证明拟议方法的有效性和优势.
结论:
- 基于探测器的边界控制方法有效地实现了被动同步研究的神经网络.
- 与现有方法相比,这种方法提供了一个实用且具有成本效益的解决方案.
- 拟议的框架为处理不同级别的检测信息提供了一种一般方法.
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