Power quality monitors (PQM) are required to be installed in a power supply network in order to assess power quality (PQ) disturbances such as voltage sags. However, with few PQMs installation, it is difficult to pinpoint the exact location of voltage sag. This paper proposes a new method for identifying the voltage sag source location by using the artificial neural network (ANN). Radial basis ...

A new approach, consisting of using radial basis function networks to obtain the long-range part of diatomic potential energy functions from simulated second virial coefficients, is presented. From these simulated data the artiÐcial neural network was able not only to learn but also to predict properties for systems that were not considered during the training process. Fifteen di†erent diatomic...

This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis fu...

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...

Input selection is advantageous in regression problems. For example, it might decrease the training time of models, reduce measurement costs, and circumvent problems of high dimensionality. Inclusion of useless inputs into the model increases also the likelihood of overfitting. Neural networks provide good generalization in many cases, but their interpretability is usually limited. However, sel...

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.

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...

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...