采用系统聚类的方法，选择待估算工程的有效样本类，确定最佳样本数量。
Then, applies the system clustering method to divide the samples into categories and select those which contain the project to be estimate, determines the optimal number of samples.
首先，采用动态聚类方法，将整个系统分解为几个子系统。
First, the whole system was decomposed into several subsystems by adopting fuzzy k-means cluster.
以地质综合分析的定性分析结论为基础，采用灰色系统理论中的灰聚类方法建立的剩余油定性描述量化模型，实现了剩余油潜力大小的定量化评价。
Based on the qualitative analysis results of geology and employing grey-clustering analysis method, the quantitatively discriminating pattern of the size of remaining oil potential is founded.
采用减法聚类辅助模糊推理系统进行电力系统短期负荷预测。
The application of Subtractive Clustering Fuzzy Inference System model to forecast short-term load is presented.
方法采用队列随访调查的方法收集居民疫水暴露的情况，并用系统聚类法中样品聚类(Q型)进行聚类分析。
Methods The data of resident's contacting infectious water were collected with cohort study, and were analyzed by sample cluster analysis (Q type).
本文依据灰色系统理论，采用灰色定权聚类方法，根据国家环境质量标准，对南京市近三年地表水的质量进行评估。
According to the National Standard of Environmental Quality, we analyzed and assessed the surface water quality in Nanjing in the recent three years.
并采用聚类搜索、专家系统对生产目标函数进行了优化，以实现操作参数的优化控制。
And the target function was deduced. The optimization parameters was calculated using the cluster searching, expert system.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
利用迭代自组织聚类法对AOI系统的检测视场进行了规划，采用基于分区搜索改进的模拟退火算法对检测路径进行了优化。
And then SAA based on regional research is adopted to optimize the inspection path of AOI. At last, a fast inspection algorithm is proposed to improve the efficiency of AOI.
文章采用层次分析法和德尔菲法相结合的方法进行城市土地集约利用评价，并运用系统聚类法对其结果进行检验。
The paper utilizes the AHP and Delphi methods to evaluate the level of land intensive use, and utilizes the hierarchical cluster analysis to check the evaluation results.
文章采用层次分析法和德尔菲法相结合的方法进行城市土地集约利用评价，并运用系统聚类法对其结果进行检验。
The paper utilizes the AHP and Delphi methods to evaluate the level of land intensive use, and utilizes the hierarchical cluster analysis to check the evaluation results.
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