Active queue control aims to improve the overall communication network throughput, while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in...

Mesh-free methods based on radial basis function (RBF) approximation are widely used for solving PDE problems. They are flexible with respect to the problem geometry and highly accurate. A disadvantage of these methods is that the linear system to be solved becomes dense for globally supported RBFs. A remedy is to introduce localisation techniques such as partition of unity (PU). RBF-PU methods...

Fraudulent emails can be detected by extraction of authorship information from the contents of emails. This paper presents information extraction based on unique words from the emails. These unique words will be used as representative features to train Radial Basis function (RBF). Final weights are obtained and subsequently used for testing. The percentage of identification of email authorship ...

We present a method of modifyiog the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node’s function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node’s center. This type of respons...

In this paper, a novel hybrid Radial Basis Function Neural Network (RBF–NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K–PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to t...

In order to guarantee security and stable operation of electric vehicle, it is necessary to on-line estimation for the state of charge (SOC) of batteries. The power battery is a complex nonlinear system, and Radial Basis Function Neural Network (RBF NN) has advantages in solving nonlinear problems, so the model of on-line SOC estimation based on RBF NN is proposed. In order to improve the predi...

Radical Basis Function (RBF) networks have been widely used in time series prediction because of their simplicity, robustness, good approximation and generalization ability. However, it is still rather difficult to select the number and locations of the hidden units of the RBF network appropriately for a specific time series prediction problem. In this paper, the Generalized RBF networks have b...

A signal su ers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either mul...

A novel reduced complexity Radial Basis Function (RBF) neural network based equaliser, referred to as the In-phase/Quadrature-phase RBF Equaliser (I/Q-RBFEQ), is proposed. The I/Q-RBF-EQ is employed in the context of turbo equalisation (TEQ) assisted by iterative channel estimation. The performance of the I/Q-RBF-TEQ is characterized in a noise limited environment over an equally weighted, symb...

In this paper, the chaotic time series RBF neural network model was designed. A prediction method for underwater acoustic chaotic signal based on RBF neural network is proposed in this paper according to the characteristics of chaotic signal with the short-term prediction. Typical Henon chaotic signal and the actual underwater acoustic chaotic signal are respectively predicted by the RBF neural...