The consensus problem of a linear discrete-time multi- agent system with directed communication topologies was investigated. A protocol was designed to solve consensus with an improved convergence speed achieved by de...The consensus problem of a linear discrete-time multi- agent system with directed communication topologies was investigated. A protocol was designed to solve consensus with an improved convergence speed achieved by designing protocol gains. The clo6ed-loop multi.agent system converged to an expected type of consensus function, which was divided into four types： zero, non- zero constant vector, bounded trajectories, and ramp trajectories. An algorithm was further provided to construct the protocol gains, which were determined in terms of a classical pole placement algorithm and a modified algebraic Riccati equation. Finally, an example to illustrate the effectiveness of theoretical results was presented.展开更多
In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique.Fuzzy clustering ensemble approaches attempt to impr...In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique.Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks.However,in these approaches,cluster(or clustering)reliability has not paid much attention to.Ignoring cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering methods.In this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus functions.Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation.Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble.To do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data objects.Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets.The experimental results demonstrate efciency and suitability of the proposed method.展开更多
基金Natural Science Foundation of Shandong Province,China(No.ZR2010FZ001)
文摘The consensus problem of a linear discrete-time multi- agent system with directed communication topologies was investigated. A protocol was designed to solve consensus with an improved convergence speed achieved by designing protocol gains. The clo6ed-loop multi.agent system converged to an expected type of consensus function, which was divided into four types： zero, non- zero constant vector, bounded trajectories, and ramp trajectories. An algorithm was further provided to construct the protocol gains, which were determined in terms of a classical pole placement algorithm and a modified algebraic Riccati equation. Finally, an example to illustrate the effectiveness of theoretical results was presented.
文摘In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique.Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks.However,in these approaches,cluster(or clustering)reliability has not paid much attention to.Ignoring cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering methods.In this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus functions.Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation.Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble.To do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data objects.Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets.The experimental results demonstrate efciency and suitability of the proposed method.
