CAJ | 학술논문

针对HTE（Hierarchical Topology Estimation ）算法计算复杂度较高的问题及其在节点相关性估计方差较大条件下性能下降的问题，提出基于合并分层聚类的网络拓扑推断算法。该算法采用自底向上的合并分层聚类，每次聚类仅使用与最大相关节点对有关的数据，相对HTE算法降低了运算复杂度；建立了改进的有限混合模型，增加了参数推断的有效数据，提高了算法的参数估计精度。仿真结果表明相对HTE算法，本文方法可以更快地推断出网络拓扑，且在节点相关性估计方差较大条件下，有更高的拓扑推断精度。
침대HTE（Hierarchical Topology Estimation ）산법계산복잡도교고적문제급기재절점상관성고계방차교대조건하성능하강적문제，제출기우합병분층취류적망락탁복추단산법。해산법채용자저향상적합병분층취류，매차취류부사용여최대상관절점대유관적수거，상대HTE산법강저료운산복잡도；건립료개진적유한혼합모형，증가료삼수추단적유효수거，제고료산법적삼수고계정도。방진결과표명상대HTE산법，본문방법가이경쾌지추단출망락탁복，차재절점상관성고계방차교대조건하，유경고적탁복추단정도。
Considering the high complexity of HTE (Hierarchical Topology Estimation )and its performance degradation under the condition of large correlation estimation variance ,a method based on agglomerative hierarchical lustering is proposed .The method employs bottom-up agglomerative hierarchical clustering ,which only uses the data related to the node pair with the largest correlation ,so it has lower computation complexity than HTE .A modified finite mixture model is established ,increasing the amount of effective data ,which improves the accuracy of parameter estimation .The simulation demonstrates that the proposed method infers the topology more rapidly ,with higher accuracy when the correlation estimation variance is large .