A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theor...A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.展开更多
With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,...With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.展开更多
为应对大规模分布式光伏(photovoltaic,PV)接入引起的主动配电网电压越限问题,降低控制策略的时序复杂性,提出一种考虑节点功率储备与节点影响力(global importance of each node,GIN)的主动配电网动态集群电压控制方法。首先,通过考虑...为应对大规模分布式光伏(photovoltaic,PV)接入引起的主动配电网电压越限问题,降低控制策略的时序复杂性,提出一种考虑节点功率储备与节点影响力(global importance of each node,GIN)的主动配电网动态集群电压控制方法。首先,通过考虑系统各节点的功率储备度,定义聚类算法的电压灵敏度-功率储备度(voltage sensitivity-power reserve,VS-PR)综合电气距离量度。进而,以GIN算法改进亲和力传播(affinity propagation,AP)聚类算法,实现网络集群划分与主导节点选取。然后,建立主动配电网集群电压控制模型,并通过动态粒子群算法(dynamic particle swarm optimization,D-PSO)进行模型求解。最后,通过建立基于MATLAB 2021b平台的IEEE 33节点仿真算例对比分析,验证了所提动态集群划分与电压控制方法的正确性和有效性。展开更多
基金supported by the National Natural Science Foundation of China (Nos. 41074003 and 60975039)the Opening Foundation of the Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No. IIP2010-1)the Youth Science Foundation of China University of Mining and Technology (Nos. 2008A045 and 2009A053)
文摘A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122 and 61071105)
文摘With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.
