In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponent...In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.展开更多
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ...Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.展开更多
Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the o...Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases.展开更多
针对基于路径分裂策略辅助极化码串行抵消列表(Path Splitting Selecting strategy based on Search Set under the Successive Cancellation List,PSS-SS-SCL)译码算法性能的不足,提出了一种利用增强型奇偶校验码改进PSSSS-SCL译码算法...针对基于路径分裂策略辅助极化码串行抵消列表(Path Splitting Selecting strategy based on Search Set under the Successive Cancellation List,PSS-SS-SCL)译码算法性能的不足,提出了一种利用增强型奇偶校验码改进PSSSS-SCL译码算法的EPC-MS-SCL(Enhanced Parity Check and Monte Carlo Segment aided Successive Cancellation List)译码算法.该算法在极化码编码阶段对信息序列做分段处理,在每段末尾添加增强型奇偶校验码,译码器仅在译码搜索集内元素时进行路径分裂,其余元素直接执行硬判决译码,并在译码完一段序列后立即对该段进行校验,仅保留通过校验的路径,从而减少了错误路径对正确路径的竞争,使正确路径保留到译码结束的概率增加,改善了译码性能,同时减少了译码列表数,使得译码复杂度更低.仿真结果表明,与PSS-SS-SCL译码算法相比较,所提出算法能在一定程度上改善其性能增益且具有更低的译码复杂度.展开更多
文摘In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.
基金supported by the National Natural Science Foundation of China(61401363)the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation(20155153034)+1 种基金the Fundamental Research Funds for the Central Universities(3102016AXXX0053102015BJJGZ009)
文摘Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases.
文摘针对基于路径分裂策略辅助极化码串行抵消列表(Path Splitting Selecting strategy based on Search Set under the Successive Cancellation List,PSS-SS-SCL)译码算法性能的不足,提出了一种利用增强型奇偶校验码改进PSSSS-SCL译码算法的EPC-MS-SCL(Enhanced Parity Check and Monte Carlo Segment aided Successive Cancellation List)译码算法.该算法在极化码编码阶段对信息序列做分段处理,在每段末尾添加增强型奇偶校验码,译码器仅在译码搜索集内元素时进行路径分裂,其余元素直接执行硬判决译码,并在译码完一段序列后立即对该段进行校验,仅保留通过校验的路径,从而减少了错误路径对正确路径的竞争,使正确路径保留到译码结束的概率增加,改善了译码性能,同时减少了译码列表数,使得译码复杂度更低.仿真结果表明,与PSS-SS-SCL译码算法相比较,所提出算法能在一定程度上改善其性能增益且具有更低的译码复杂度.
文摘针对三支高斯混合聚类算法(three-way Gaussian mixture model,T-GMM)的阈值通常为人为设定,增加算法的不确定性的问题,本文中将阴影集思想融入三支高斯混合模型,提出一种基于阴影集的三支高斯混合聚类算法(three-way Gaussian mixture model clustering based on shadow sets,ST-GMM);ST-GMM算法先构造一个关于阈值的目标函数,再通过优化算法选取最优阈值。基于10个不同类型的UCI数据集的实验结果表明:ST-GMM算法不仅继承了T-GMM算法的特点,同时有效地降低了人为设定阈值的误差,聚类细节的刻画也更加准确。针对评价指标的测试进一步验证了ST-GMM算法具有良好的聚类性能。
