为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇...为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性。展开更多
针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别...针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。展开更多
Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real proc...Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints （KSC） algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive （MVI） kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.展开更多
针对传统的自回归模型和自回归移动平均模型在齿轮箱早期异常检测中准确性不足的问题,采用有源自回归模型(autoregressive with extra inputs model,ARX)和统计过程控制相结合的方法进行齿轮箱早期异常检测。首先,对原始振动数据进行时...针对传统的自回归模型和自回归移动平均模型在齿轮箱早期异常检测中准确性不足的问题,采用有源自回归模型(autoregressive with extra inputs model,ARX)和统计过程控制相结合的方法进行齿轮箱早期异常检测。首先,对原始振动数据进行时域同步平均降噪处理;然后考虑到负载变化对输出信号的影响,提取信号的包络表征负载变化信息并作为模型的输入结合赤池信息准则(akaike information criterion,AIC)和最小二乘法建立模型;最后分别采用统计过程控制、支持向量数据描述(support vector data description,SVDD)、核主成分分析(kernel principal component analysis,KPCA)对残差数据的均方根值进行处理。结果表明,ARX模型结合指数加权移动平均(exponential weighed moving average,EWMA)控制图在第44个文件发现早期异常,相比于自回归模型、自回归移动平均模型、SVDD和KPCA分别提前11、6个、10和11个文件检测出异常,从而验证了该方法的可行性和有效性,对齿轮箱早期异常检测有重要意义。展开更多
文摘为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性。
文摘针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。
基金supported by Key Discipline Construction Program of Beijing Municipal Commission of Education (XK10008043)
文摘Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints （KSC） algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive （MVI） kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.
文摘针对传统的自回归模型和自回归移动平均模型在齿轮箱早期异常检测中准确性不足的问题,采用有源自回归模型(autoregressive with extra inputs model,ARX)和统计过程控制相结合的方法进行齿轮箱早期异常检测。首先,对原始振动数据进行时域同步平均降噪处理;然后考虑到负载变化对输出信号的影响,提取信号的包络表征负载变化信息并作为模型的输入结合赤池信息准则(akaike information criterion,AIC)和最小二乘法建立模型;最后分别采用统计过程控制、支持向量数据描述(support vector data description,SVDD)、核主成分分析(kernel principal component analysis,KPCA)对残差数据的均方根值进行处理。结果表明,ARX模型结合指数加权移动平均(exponential weighed moving average,EWMA)控制图在第44个文件发现早期异常,相比于自回归模型、自回归移动平均模型、SVDD和KPCA分别提前11、6个、10和11个文件检测出异常,从而验证了该方法的可行性和有效性,对齿轮箱早期异常检测有重要意义。
