A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy syst...A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.展开更多
To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retr...To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.展开更多
鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种...鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。展开更多
针对模糊C有序均值聚类算法没有考虑图像空间信息,导致难以有效地分割含噪图像的问题,提出一种基于非局部信息和子空间的模糊C有序均值聚类(non-local information and subspace for fuzzy C-ordered means,SFCOM-NLS)算法.首先,利用图...针对模糊C有序均值聚类算法没有考虑图像空间信息,导致难以有效地分割含噪图像的问题,提出一种基于非局部信息和子空间的模糊C有序均值聚类(non-local information and subspace for fuzzy C-ordered means,SFCOM-NLS)算法.首先,利用图像中给定的相似邻域结构的像素提取当前像素的非局部空间信息;其次,计算每个像素的典型性,并对其进行排序,在每次迭代中更新像素的典型性,提高像素聚类的准确性,解决在聚类过程中存在相似类导致的误分类问题;最后,引入子空间聚类概念,为图像不同维度分配适当的权重,提高彩色图像的分割性能.在含噪合成图像和公开数据集BSDS500,MSRA100和AID上实验结果表明,所提算法的模糊划分系数、模糊划分熵、分割精度和标准化互信息平均值分别达到了95.00%,6.66%,98.77%和95.54%,均优于对比的同类算法.展开更多
随着数据交易市场的兴起,数据价值评估成为关键技术问题。尽管数据夏普利值是一种公平的数据价值度量方法,但其高昂的计算成本和对数据复制攻击缺乏抵御能力,严重限制了在实际数据交易场景中的应用。提出了一种高效且具备复制鲁棒性的...随着数据交易市场的兴起,数据价值评估成为关键技术问题。尽管数据夏普利值是一种公平的数据价值度量方法,但其高昂的计算成本和对数据复制攻击缺乏抵御能力,严重限制了在实际数据交易场景中的应用。提出了一种高效且具备复制鲁棒性的数据交易估值框架。针对数据夏普利值计算效率低下的问题,优化了数据集合效用计算后的更新策略,提出了一种高效的数据夏普利值近似算法OA-Shapley(one for all Shapley)。该算法通过单次效用计算更新所有数据点的夏普利值,显著提高了计算效率,并在理论上保证了算法的无偏性和均方误差。针对数据复制攻击问题,从理论上推导出严格冗员性是复制鲁棒性的充分条件,并基于此提出了CL+Shapley(Cluster+Shapley)数据估值框架。该框架通过聚类预处理实现严格冗员性,能够有效抵御数据复制攻击,并且与具体的数据夏普利算法解耦,具有广泛的适用性。实验结果表明,OA-Shapley算法在去除高(低)价值数据点实验中,AUC指标优于基线算法12.4%(3.5%),无效数据检出量增加9%~32%。CL+Shapley框架在复制攻击实验中展现出优异的复制鲁棒性。展开更多
基金Project(61473298)supported by the National Natural Science Foundation of ChinaProject(2015QNA65)supported by Fundamental Research Funds for the Central Universities,China
文摘A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.
基金Supported by National Natural Science Foundation of China （60874063）, and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
基金This project was supported by National High Tech Foundation of 863 (2001AA115123)
文摘To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.
文摘鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。
文摘针对模糊C有序均值聚类算法没有考虑图像空间信息,导致难以有效地分割含噪图像的问题,提出一种基于非局部信息和子空间的模糊C有序均值聚类(non-local information and subspace for fuzzy C-ordered means,SFCOM-NLS)算法.首先,利用图像中给定的相似邻域结构的像素提取当前像素的非局部空间信息;其次,计算每个像素的典型性,并对其进行排序,在每次迭代中更新像素的典型性,提高像素聚类的准确性,解决在聚类过程中存在相似类导致的误分类问题;最后,引入子空间聚类概念,为图像不同维度分配适当的权重,提高彩色图像的分割性能.在含噪合成图像和公开数据集BSDS500,MSRA100和AID上实验结果表明,所提算法的模糊划分系数、模糊划分熵、分割精度和标准化互信息平均值分别达到了95.00%,6.66%,98.77%和95.54%,均优于对比的同类算法.
文摘随着数据交易市场的兴起,数据价值评估成为关键技术问题。尽管数据夏普利值是一种公平的数据价值度量方法,但其高昂的计算成本和对数据复制攻击缺乏抵御能力,严重限制了在实际数据交易场景中的应用。提出了一种高效且具备复制鲁棒性的数据交易估值框架。针对数据夏普利值计算效率低下的问题,优化了数据集合效用计算后的更新策略,提出了一种高效的数据夏普利值近似算法OA-Shapley(one for all Shapley)。该算法通过单次效用计算更新所有数据点的夏普利值,显著提高了计算效率,并在理论上保证了算法的无偏性和均方误差。针对数据复制攻击问题,从理论上推导出严格冗员性是复制鲁棒性的充分条件,并基于此提出了CL+Shapley(Cluster+Shapley)数据估值框架。该框架通过聚类预处理实现严格冗员性,能够有效抵御数据复制攻击,并且与具体的数据夏普利算法解耦,具有广泛的适用性。实验结果表明,OA-Shapley算法在去除高(低)价值数据点实验中,AUC指标优于基线算法12.4%(3.5%),无效数据检出量增加9%~32%。CL+Shapley框架在复制攻击实验中展现出优异的复制鲁棒性。
基金Supported by National Natural Science Foundation of China （60874063） and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
