Modeling Depression Data: Feed Forward Neural Network vs. Radial Basis Function Neural Network

A Comparative Study of Multilayer Feed-forward Neural Network and Radial Basis Function Neural Network Models for Speech Recognition

The most common way of human-to-human communication is speech. As speech provides the easiest and most natural way of interaction, it becomes the need of human-to-machine communication as well. Automatic speech recognition (ASR) is the technology to enable machines to understand process and recognize speech. Due to its applicability in various application domains, ASR becomes one of the most fa...

Signal Prediction by Layered Feed - Forward Neural Network (RESEARCH NOTE).

In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

Radial Basis Neural Network Based Islanding Detection in Distributed Generation

This article presents a Radial Basis Neural Network (RBNN) based islanding detection technique. Islanding detection and prevention is a mandatory requirement for grid-connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone (NDZ), concern has been raised on active method ...

A supervised radial basis function neural network

Median radial basis function neural network

Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approa...