The performance of a Radial Basis Functions network (RBF) can be increased with the use of an ensemble of RBF networks because the RBF networks are successfully applied to solve classification problems and they can be trained by gradient descent algorithms. Reviewing the bibliography we can see that the performance of ensembles of Multilayer Feedforward (MF) networks can be improved by the use ...

We examine the ARCH-GARCH models for the forecasting of the bond price time series provided by VUB bank and make comparisons the forecast accuracy with the class of RBF neural network models. A limited statistical or computer science theory exists on how to design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynami...

This paper introduces a neural network approach to the problem of ore grade estimation. The system under consideration consists of three neural network modules each responsible for a different area of the deposit, depending on the sampling density. Octant and quadrant search is used as a way of presenting input patterns to the modules. Both radial basis function networks and multi-layered perce...

This paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. In the proposed scheme, flux linkage analysis is used to reach a decision. Probabilistic neural network (PNN) and discrete wavelet transform (DWT) are used in design of fault diagnosis system. PNN as main part of thi...

The k-means training algorithm used for the RBF (Radial Basis Function) neural network can have some weakness like empty clusters, the choice of the cluster number and the random choice of the centers of theses clusters. In this paper, we use the Fuzzy Min Max technique to boost the performances of the training algorithm. This technique is used to determine the number of the k centers and to in...

@article{Bonanno2012956, author = ”F. Bonanno and G. Capizzi and G. Graditi and C. Napoli and G.M. Tina”, title = ”A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module ”, journal = ”Applied Energy ”, volume = ”97”, number = ”0”, pages = ”956 961”, year = ”2012”, issn = ”0306-2619”, doi = ”http://dx.doi.org/10.1016/j.apenerg...

A strategy for detection of feedstock variations in a continuous pulp digester is presented. A Gaussian Radial Basis Function Neural Network is used to infer these unmeasured variations. The absence of plant data motivates the development of training set data. The efficiency and limitation of the approach is demonstrated with a first principles model.

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

This paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furth...

In view of disasters caused by rock burst becoming more and more serious in coal mine production, three models are established for evaluation and prediction the rock burst risk based on artificial neural network. First, ten indicators are determined which have a larger influence on rock burst. Then two back propagation network models are trained using the original data and the processed data re...