Abstract. The performance of financial forecasting with neural networks dependents on the particular training set. We design mean-change-point test to divide the original dataset i...
Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...
Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pah...
: This paper presents a new approach to equalization of communication channels using Artificial Neural Networks (ANNs). A novel method of training the ANNs using Tabu based Back Pr...
— Efficient Training in a neural network plays a vital role in deciding the network architecture and the accuracy of these classifiers. Most popular local training algorithms t...