A novel method for constructing RBF networks is presented. It is based on Boosting, an ensemble method that combines several classifiers obtained using any other classification method. If the classifiers that are going to be combined by boosting are radialbasis functions, then the boosting method produces a RBF network as result. The method for constructing a RBF is based on obtaining a decisio...

in this paper, we propose a new numerical method for solution of urysohn two dimensional mixed volterra-fredholm integral equations of the second kind on a non-rectangular domain. the method approximates the solution by the discrete collocation method based on inverse multiquadric radialbasis functions (rbfs) constructed on a set of disordered data. the method is a meshless method, because it i...

In this paper, Multi-Layer Perceptron and RadialBasis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3and 6-h lead time prediction and the only reliable one for 9-h lead time forecas...

| Adaptive learning dynamics of the Radial Basis Function Network (RBFN) are compared with a scale-based clustering technique Won93] and a relationship between the two is pointed out. Using this link, it is shown how scale-based clustering can be done using the RBFN, with the Radial Basis Function (RBF) width as the scale parameter. The technique suggests the \right" scale at which the given da...

Although physiologically-indicative signals can be acquired in low-power biomedical sensors, their accurate analysis imposes several challenges. Data-driven techniques, based on supervised machinelearning methods provide powerful capabilities for potentially overcoming these, but the computational energy is typically too severe for low-power devices. We present a formulation for the kernel func...

In this paper, a Robust adaptive neural network controller (RANNC) based on variable structure system for robotic manipulators is proposed to alleviate the problems met in practical implementation using classical variable structure controllers. The chattering phenomenon is eliminated by substituting single-input single-output radialbasis-function neural networks (RBFNN's), which are nonlinear a...

This paper reports a new Cartesian-grid collocation method based on radialbasis-function networks (RBFNs) for numerically solving elliptic partial differential equations (PDEs) in irregular domains. The domain of interest is embedded in a Cartesian grid, and the governing equation is discretized by using a collocation approach. The new features here are (a) One-dimensional integrated RBFNs are ...

Dynamic security analysis is performed to ensure that a power system will survive any recognized contingency and will transfer to a new but acceptable steady-state condition. This article deals with one of the very important dynamic security indices – the critical clearing time. Some of the popular methods of calculating the critical clearing time (CCT) have been reviewed. A more recent method ...