对异步领导-追随者马科维神经网络的反准同步,使用隐藏的基于马科夫模型的间歇控制
IEEE transactions on cybernetics
|July 16, 2025
概括
本研究介绍了领导追随者马科维神经网络 (MNN) 的间歇控制器,以实现反准同步,尽管存在能量限制和未知模型. 隐藏的马尔科夫模型 (HMM) 方法确保了强大的同步.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 同步对于联网系统至关重要,但在具有参数不匹配的离散时间异步马科维神经网络 (MNN) 中存在挑战.
- 能源限制和未知的系统动态阻碍了这些网络的有效控制设计.
研究的目的:
- 对于具有不匹配参数的离散时间异步领袖-追随者MNN来研究反准同步.
- 制定间歇性控制策略,以应对能源限制.
- 使用隐藏的马尔科夫模型 (HMM) 来推断未知的马尔科夫模式.
主要方法:
- 采用间歇式控制传输策略来管理能源限制.
- 使用隐藏的马尔科夫模型 (HMM) 来从可观测信息中推断未知的模式.
- 基于HMM的间歇性非脆弱控制器被设计用于追随MNN.
- 用指数代方法来建立足够的条件来实现反准同步.
主要成果:
- 建立了足够的条件来实现领导者-追随者MNN中的反准同步.
- 确定了反准同步的最佳边界.
- 拟议的基于HMM的间歇控制器在模拟中证明了其有效性.
结论:
- 开发的基于HMM的间歇性控制策略有效地实现了对离散时间异步领袖-追随者MNN的反准同步.
- 该方法解决了能源限制和未知的系统动态,为同步问题提供了强大的解决方案.
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