针对高维数据集,文中提出一种PREP(PCA-Relief F for EP)算法:首先采用PCA和Relief F算法实现特征降维;然后利用EP模式思想,构造精度更高、规模更小的EP模式分类器;最后利用标准数据集对文中的方法进行测试。实验结果表明,在对高维数据...针对高维数据集,文中提出一种PREP(PCA-Relief F for EP)算法:首先采用PCA和Relief F算法实现特征降维;然后利用EP模式思想,构造精度更高、规模更小的EP模式分类器;最后利用标准数据集对文中的方法进行测试。实验结果表明,在对高维数据进行分类时,该方法构造的分类器在预测精度和运行时间上均有较大幅度的提升。展开更多
We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping...We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping is continuous and f-pseudomonotone with nonempty compact convex values on dom（f）, where f ： Rn --RU{＋∞} is a proper func- tion. The algorithm presented in this paper generalize and improve some known algorithms in literatures. Preliminary computational experience is also reported.展开更多
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo...K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.展开更多
The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component a...The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component assignments change the system reliability.The optimization of Lin/Con/k/n:F(G)system is to find an optimal component assignment to maximize the system reliability.As the number of components increases,the computation time for this problem increases considerably.In this paper,we propose a Birnbaum importance-based ant colony optimization(BIACO)algorithm to obtain quasi optimal assignments for such problems.We compare its performance using the Birnbaum importance based two-stage approach(BITA)and Birnbaum importancebased genetic local search(BIGLS)algorithm from previous researches.The experimental results show that the BIACO algorithm has a good performance in the optimization of Lin/Con/k/n:F(G)system.展开更多
With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, ne...With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide larger bandwidth and at the same time small dimensions. Although the gain in bandwidth performances of an antenna are directly related to its dimensions in relation to the wavelength, the aim is to keep the overall size of the antenna constant and from there, find the geometry and structure that give the best performance. The design and bandwidth optimization of a Planar Inverted-F Antenna (PIFA) were introduced in order to achieve a larger bandwidth in the 2 GHz band, using two optimization techniques based upon genetic algorithms (GA), namely the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). During the optimization process, the different PIFA models were evaluated using the finite-difference time domain (FDTD) method-a technique belonging to the general class of differential time domain numerical modeling methods.展开更多
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis...Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.展开更多
文摘针对高维数据集,文中提出一种PREP(PCA-Relief F for EP)算法:首先采用PCA和Relief F算法实现特征降维;然后利用EP模式思想,构造精度更高、规模更小的EP模式分类器;最后利用标准数据集对文中的方法进行测试。实验结果表明,在对高维数据进行分类时,该方法构造的分类器在预测精度和运行时间上均有较大幅度的提升。
基金supported by the Scientific Research Foundation of Sichuan Normal University(20151602)National Natural Science Foundation of China(10671135,61179033)and the Key Project of Chinese Ministry of Education(212147)
文摘We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping is continuous and f-pseudomonotone with nonempty compact convex values on dom（f）, where f ： Rn --RU{＋∞} is a proper func- tion. The algorithm presented in this paper generalize and improve some known algorithms in literatures. Preliminary computational experience is also reported.
文摘K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
基金the National Natural Science Foundation of China(Nos.71871181 and 71471147)the Overseas Expertise Introduction Project for Discipline Innovation(No.B13044)the Top International University Visiting Program for Outstanding Young Scholars of Northwestern Polytechnical University(No.201806295008)。
文摘The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component assignments change the system reliability.The optimization of Lin/Con/k/n:F(G)system is to find an optimal component assignment to maximize the system reliability.As the number of components increases,the computation time for this problem increases considerably.In this paper,we propose a Birnbaum importance-based ant colony optimization(BIACO)algorithm to obtain quasi optimal assignments for such problems.We compare its performance using the Birnbaum importance based two-stage approach(BITA)and Birnbaum importancebased genetic local search(BIGLS)algorithm from previous researches.The experimental results show that the BIACO algorithm has a good performance in the optimization of Lin/Con/k/n:F(G)system.
文摘With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide larger bandwidth and at the same time small dimensions. Although the gain in bandwidth performances of an antenna are directly related to its dimensions in relation to the wavelength, the aim is to keep the overall size of the antenna constant and from there, find the geometry and structure that give the best performance. The design and bandwidth optimization of a Planar Inverted-F Antenna (PIFA) were introduced in order to achieve a larger bandwidth in the 2 GHz band, using two optimization techniques based upon genetic algorithms (GA), namely the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). During the optimization process, the different PIFA models were evaluated using the finite-difference time domain (FDTD) method-a technique belonging to the general class of differential time domain numerical modeling methods.
文摘Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.
