一种神经网络辅助的方法,用于对未知非线性能量采集复杂网络的递归状态估计
IEEE transactions on neural networks and learning systems
|March 10, 2026
概括
本研究开发了一种基于神经网络的复杂网络的递归状态估计方法,用于具有能量收集传感器的复杂网络. 该方法有效地估计了在能量约束下系统状态和未知的非线性.
科学领域:
- 控制系统工程 控制系统工程
- 网络科学 网络科学
- 信号处理 信号处理
背景情况:
- 复杂网络 (CNs) 由于未知的非线性,在状态估计方面面临挑战.
- 能量采集传感器引入间歇性数据传输,使估计变得复杂.
- 基于部分节点 (PNB) 的递归状态估计对于监控这些系统至关重要.
研究的目的:
- 开发一个强大的递归状态估计算法,用于未知非线性CNs.
- 为了解决能源收获传感器所造成的能源限制.
- 同时估计系统状态和近似未知的非线性.
主要方法:
- 利用神经网络 (NN) 以其通用近似属性来建模未知的非线性.
- 开发了一种基于NN的递归估计算法,用于同时进行状态和非线性估计.
- 集成了传感器的能量补充机制,以管理传输成本.
主要成果:
- 成功生成了对系统状态和未知非线性值的估计.
- 在统一的框架中计算了递归状态估计器收益和NN重量 (NNW) 调整参数.
- 通过模拟示例证明了算法的有效性.
结论:
- 拟议的基于NN的递归估计算法有效地处理了CN中未知的非线性和能量约束.
- 计算增益和调整参数的统一框架提供了一种高效的方法.
- 该方法提供了一个可行的解决方案,用于用能量收集传感器在复杂网络中的状态估计.
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