论文标题
哈密顿的一些毛毛虫图的完整数量
Hamiltonian Complete Number of Some Variants of Caterpillar Graphs
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
A graph $G$ is said to be Hamiltonian if it contains a spanning cycle. In this work, we investigate the Hamiltonian completeness of certain classes of caterpillar graphs, which are trees with a central path to which all other vertices are adjacent. For a non-Hamiltonian graph $G$, the Hamiltonian complete number $λ_H(G)$ is the minimum number of edges that must be added to $G$ to make it Hamiltonian. We focus on both regular and irregular caterpillar graphs, deriving explicit formulas for $λ_H(G)$ in various cases. Specifically, we show that for a regular caterpillar graph $G_{n(k)}$ where each vertex on the central path is adjacent to $k$ leaves, $λ_H(G_{n(k)}) = n(k-1)$. We also explore irregular caterpillar graphs, where the number of leaves adjacent to each vertex on the central path varies, and provide bounds for $λ_H(G)$ in these cases. Our results contribute to the understanding of Hamiltonian properties in tree-like structures and have potential applications in network design and optimization.