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Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attentions have been drawn to solve this problem such as how to provide bandwidth guarantees for Virtualized Machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic...
A new dynamic neural network was constructed by borrowing ideas from Jordan and Elman neural networks. To accelerate the rate of convergence and avoid getting into local extremum, a hybrid learning algorithm by Genetic algorithm (GA) and error back propagation algorithm (BP) was used to tune the weight values of the network. Finally, the improved neural network was utilized to identify the AUV hydrodynamic...
As a new evolutionary algorithm, particle swarm optimization (PSO) algorithm has been gained much attention and wide applications in different fields during the past decade. However, for nonlinear, no differentiable and multi-modal problems, the PSO algorithm often suffers the problem of being trapped in local optima so as to be premature convergence. To enhance the performance of standard PSO, the...
The radial basis function (RBF), which is well known dynamic neural network, has been improved to easily apply in dynamic systems identification. However, the RBF weights and thresholds, which are trained by the gradient descent method, will be fixed after the training completing. The adaptive ability is bad. To improve RBF performance of dynamic identification, a self-adaptive particle swarm optimization...
The radial basis function (RBF) is well known dynamic recursion neural network. However, RBF weights and thresholds, which are trained by back propagation algorithm, the gradient descent method and genetic algorithm, will be fixed after the training completing. The adaptive ability is bad. To improve RBF identification performance, particle swarm optimization (PSO), which is a stochastic search algorithm,...
Original particle swarm optimization (OPSO) algorithm was modified in the paper, and a self-adaptive PSO (SPSO) was proposed. In this algorithm, SPSO combines Elman neural network (ENN) and forms SPSO-ENN hybrid algorithm. Compared with ENN algorithm, the experiment results show that SPSO-ENN has less adjustable parameters, faster convergence speed and higher precision in the nonlinear function identification.
There is a growing interest in subspace discriminative feature extraction techniques based on tensor (multilinear) representation, which encodes an image object as a general tensor of second or even higher order. However, on one hand the computational convergence of its iterative algorithms is not guaranteed, on the other these methods are impractical for real-time applications for large training...
Probability theory is a branch of mathematics dealing with chance phenomena and has clearly discernible links with the real world. It is the framework foundations of many applying subjects, such as information theory, mathematics risk theory and Insurance theory for actuaries etc. The strong deviation theorems is one of the central questions for studying in the international probability theory . In...
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