In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using th...

Pattern classification was an important part of the RBF neural network application. When the electronic nose is concerned, in many cases it is difficult to obtain the entire representative sample; it requires frequent updating the sample libraries and re-training the electronic nose. In addition,the gas detected from the online environment is not always the known gas in the training samples. Th...

This paper presents the pruning and model-selecting algorithms to the support vector learning for sample classification and function regression. When constructing RBF network by support vector learning we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensi...

In this paper we propose a novel training algorithm for RBF networks that is based on extended kalman filter and fuzzy logic.After the user choose how many prototypes to include in the network, the extended kalman filter simultancously solves for the prototype vectors and the weight matrix.The fuzzy logic is used to cope with the devergence problem caused by the insufficiently known a priori fi...

According to the high accuracy of load model in power system, a novel dynamic population variation genetic programming with Kalman operator for load model in power system is proposed. First, an evolution load model called initial model in power system evolved by dynamic variation population genetic programming is obtained which has higher accuracy than traditional models. Second, parameters in ...

Radial Basis Functions (RBF) have found a wide area of applications. We consider the case of polyharmonic RBF (called sometimes polyharmonic splines) where the data are on special grids of the form Z× aZn having practical importance. The main purpose of the paper is to consider the behavior of the polyharmonic interpolation splines Ia on such grids for the limiting process a → 0, a > 0. For a l...

The task of faults localization is discussed in a model-free setting. As a tool for its solution we consider a multiclass pattern recognition problem with a metric in the label space. Then, this problem is approximately solved, providing hints on selecting appropriate RBF nets. It was shown that the approximate solution is the exact one in several important cases. Finally, we propose the algori...

This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the autotuning method of learning rate and momentum is proposed and applied...

The typical system identi cation procedure requires powerful and versatile software means. In this paper we describe and exemplify the use of a prototype identi cation software tool, applicable for the rather broad class of multi input single output model structures with regressors that are formed by delayed inand outputs. Interesting special instances of this model structure category include, ...

In this addition to the regular paper, we derive the required derivatives required to implement the informative prior from a simulator in PILCO [1]. First, for completeness, we repeat the derivation of the mean, covariance, and input-output covariance of the predictive mean of a Gaussian process (GP) when the prior mean is a radial basis function network (RBF). Then, we detail the partial deriv...