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...

Neural networks are investigated for predicting the magnitude of the largest seismic event in the following month based on the analysis of eight mathematically computed parameters known as seismicity indicators. The indicators are selected based on the Gutenberg-Richter and characteristic earthquake magnitude distribution and also on the conclusions drawn by recent earthquake prediction studies...

| Successful implementations of radial-basis function (RBF) networks for classiication tasks must deal with architectural issues, the burden of irrelevant attributes, scaling , and some other problems. This paper addresses these issues by initializing RBF networks with decision trees that deene relatively pure regions in the instance space; each of these regions then determines one basis functi...

In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GR...

The instantaneous assessment of high-priced liquor products with minimal sample volume and no special preparation is an important task for quality monitoring and fraud detection. In this contribution the automated classification of Raman spectra acquired with a special optofluidic chip is performed with the use of a number of Artificial Neural Networks. A standard Radial Basis Function Network ...

[1] K. J. Hunt, R. Hass, and R. Murray-Smith, “Extending the functional equivalence of radial basis function networks and fuzzy inference systems,” IEEE Trans. Neural Networks, vol. 7, pp. 776–781, May 1996. [2] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, MATLAB Curriculum Series. Upper Saddle River, N...

In this work we jointly analyze the performance of three classic Artificial Neural Network models and one Support Vector Machine with respect to a series of data complexity measures known as measures of separability of classes. In particular, we consider a Radial Basis Function Network, a Multi-Layer Perceptron, a Learning Vector Quantization, while the Sequential Minimal Optimization method is...

This paper presented the Minimal Radial Basis Function Network (MRBFN) approach busbar protection. The Optical Current Transducer (OCT) is used to solve the magnetic saturation so as to improve the reliability of the system. Performance of this model is compared with Feed Forward Back Propagation Neural Network (FFBP). The proposed model is more accurate in prediction with few numbers of hidden...

This paper analyses the application of Radial Basis Function (RBF) networks in grade interpolation. These networks are a very unique member of the family of Artificial Neural Networks. RBF networks have such theoretical properties that establish them as a potential alternative to existing grade interpolation techniques. Their suitability to the problem of grade interpolation will be demonstrate...