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...

Two difficulties are involved with traditional RBF networks: the initial configuration of an RBF network needs to be determined by a trial-and-error method, and the performance suffers degradation when the desired locations of the center of the RBF are not suitable. A novel RBF network is proposed to overcome these difficulties. A new radial basis function is used for hidden nodes, and the numb...

In this paper we engineer an information mapping of transmission linkages across various European government bond markets. The research introduces a calibration methodology for the application of an optimizing radial basis function (RBF) artificial neural network (ANN). Utilizing a closed-form derivation of the regularization parameter, the Kajiji-4 RBF ANN is known to efficiently minimize the ...

Several investigations around the world have been postulated that the infant cry can be utilized to asses the infant’s status and the use of Artificial Neural Networks (ANN) has been one of the recent alternatives to classify cry signals [4,9]. A Radial Basis Function (RBF) network is implemented for infant cry classification in order to find out relevant aspects concerned with the presence of ...

In this short note we analyze the performance of Backpropagation Neural Network (BPNN), Radial Basis Function Network (RBFN), Classification Based on Multiple Association Rule (CMAR) and Classification Based on Association (CBA) on mammographic mass data from UCI repository. The performance of the classifier is evaluated using sensitivity, specificity and classification accuracy.

This article describes a new structure to create a RBF neural network; this new structure has 4 main characteristics: firstly, the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (w...

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial bas...

We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture d...

The selection of centers and widths has a strong influence on the performance of radial basis function neural network classifier. In this paper, a novel approach of clustering based on Fuzzy Cmeans clustering is proposed, which is called cooperative clustering, and use it for selection of centers of radial basis function neural network. Experimental results show that the performance of classifi...