A kind of dispatch method for power system eigenvalue control is proposed-in this paper. With the help of this method, not only the low-frequency oscillation of a power system can be prevented and controlled, but also...A kind of dispatch method for power system eigenvalue control is proposed-in this paper. With the help of this method, not only the low-frequency oscillation of a power system can be prevented and controlled, but also the probabilistic power oscillatoin on the interconnection lines of an interconnected power system can be reduced. The proposed method has the advantages of high calculation speed and good convergency. Therefore, the method has much prospect of on-line application.展开更多
Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network...Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.展开更多
文摘A kind of dispatch method for power system eigenvalue control is proposed-in this paper. With the help of this method, not only the low-frequency oscillation of a power system can be prevented and controlled, but also the probabilistic power oscillatoin on the interconnection lines of an interconnected power system can be reduced. The proposed method has the advantages of high calculation speed and good convergency. Therefore, the method has much prospect of on-line application.
基金Project(2007CB311106) supported by National Key Basic Research Program of ChinaProject(NEUL20090101) supported by the Foundation of National Information Control Laboratory of China
文摘Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.
