Partial differential equations (PDEs) with boundary conditions (Dirichlet or Neumann) defined on boundaries with simple geometry have been successfully treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a r...

A Bayesian-based methodology is presented which leads to a data analysis system based around a committee of radial-basis function (RBF) networks. We show that this approach enables estimatation of the uncertainty associated with system outputs. Systems with diiering numbers of internal degrees of freedom (weights) may hence be compared using training data only. Feedforward neural networks have ...

The main aim of this paper is to determine to analyze the electrical transfer capability among different electricity markets using repeated power flow technique. Instead of minimizing the total cost in the conventional problem, in the paper, the transfer capability between two markets or two electricity supply and generation areas is maximized. To reduce the time required to compute transfer ca...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the classifiers to combine are RBF Networks. This ensemble method, for each member of the ensemble, transforms the data set using a pseudo-random rotation of the axis. Then the classifier is constructed using this rotation data. The re...

The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illuminat...

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...

Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It has been shown in [1] that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron subnetworks, is c...

This paper presents an alternating minimization algorithm used to train radial basis function networks. The algorithm is a modiication of an interior point method used in solving primal linear programs. The resulting algorithm is shown to have a convergence rate on the order of p nL iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy.

Selecting a set of features which is optimal for a given classiication task is one of the central problems in machine learning. We address the problem using the exible and robust lter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and therefore a solution in the feature selection sense and also gives detailed information about feature r...