间歇控制下的权重复杂网络的拓识别及其在神经网络中的应用
IEEE transactions on neural networks and learning systems
|March 10, 2025
概括
本研究引入了一种新的方法,用于识别使用非周期间歇控制 (AIC) 的随机复杂网络拓. 这种方法克服了以前方法的局限性,使得即使没有连续反控制,也可以识别拓.
科学领域:
- 网络科学 网络科学
- 复杂的系统复杂的系统.
- 控制理论 控制理论
背景情况:
- 拓识别对于理解随机复杂网络至关重要.
- 现有的方法通常依赖于持续反控制,这限制了它们的适用性.
- 间歇性间歇性控制 (AIC) 由于其固有的休息时间而带来了挑战.
研究的目的:
- 为在非周期间歇控制 (AIC) 下的随机复杂网络开发新的拓识别标准.
- 为了解决网络识别中持续反控制的局限性.
- 提出一种具有成本效益和操作简单性的方法.
主要方法:
- 图形理论方法和随机分析技术的整合.
- 鉴定标准的衍生基于几乎肯定地指数级同步的驱动响应网络.
- 应用AIC,考虑到其休息时间,用于拓识别.
主要成果:
- 在AIC下建立了用于随机复杂网络的新型拓识别标准.
- 提出的方法几乎可以肯定地实现驱动响应网络的指数级同步.
- 使用神经网络和数值模拟验证了这些标准,证明了它们的有效性.
结论:
- 该研究成功地提出了在AIC下用于随机网络的第一个拓识别标准.
- 与连续控制方法相比,综合方法提供了更灵活,更实用的解决方案.
- 包含调节切换扩散,多重量和非线性合,提高了该模型的适用性.
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