Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input da...Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.展开更多
This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the sur...This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.展开更多
A distributed and adaptive framework (DAF) was proposed for provisioning quality of service (QoS) in IPv6 network. In DAF, per-flow admission control and resource reservation, in conjunction with a new IPv6 flow label...A distributed and adaptive framework (DAF) was proposed for provisioning quality of service (QoS) in IPv6 network. In DAF, per-flow admission control and resource reservation, in conjunction with a new IPv6 flow label mechanism, can be performed instantaneously in a fully distributed and independent fashion at the edge of network without hop-by-hop signaling. The flow label helps in resource reservation and packets forwarding for aggregated traffic on an edge-to-edge path basis. In addition, a bounded directional probing technique for DAF was designed to reconfigure resource reservation adaptively between every pair of edge router for aggregated traffic according to the fluctuation of its traffic load. The simulation results show that DAF provides QoS guarantees to individual flows with minimal overhead, as well as keeping the scalability characteristic like DiffServ.展开更多
文摘Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.
文摘This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.
文摘A distributed and adaptive framework (DAF) was proposed for provisioning quality of service (QoS) in IPv6 network. In DAF, per-flow admission control and resource reservation, in conjunction with a new IPv6 flow label mechanism, can be performed instantaneously in a fully distributed and independent fashion at the edge of network without hop-by-hop signaling. The flow label helps in resource reservation and packets forwarding for aggregated traffic on an edge-to-edge path basis. In addition, a bounded directional probing technique for DAF was designed to reconfigure resource reservation adaptively between every pair of edge router for aggregated traffic according to the fluctuation of its traffic load. The simulation results show that DAF provides QoS guarantees to individual flows with minimal overhead, as well as keeping the scalability characteristic like DiffServ.
