Title

A generalized feedforward neural network architecture for classification and regression

Document Type

Journal Article

Publisher

Elsevier Ltd

Faculty

Computing, Health and Science

School

Computing, Health and Science Faculty Office

RAS ID

2330

Comments

Originally published as: Arulampalam, G., & Bouzerdoum, A. (2003). A generalized feedforward neural network architecture for classification and regression. Neural networks, 16(5-6), 561-568. Original article available here

Abstract

This article presents a new generalized feedforward neural network (GFNN) architecture for pattern classification and regression. The GFNN architecture uses as the basic computing unit a generalized shunting neuron (GSN) model, which includes as special cases the perceptron and the shunting inhibitory neuron. GSNs are capable of forming complex, nonlinear decision boundaries. This allows the GFNN architecture to easily learn some complex pattern classification problems. In this article the GFNNs are applied to several benchmark classification problems, and their performance is compared to the performances of SIANNs and multilayer perceptrons. Experimental results show that a single GSN can outperform both the SIANN and MLP networks.

DOI

10.1016/S0893-6080(03)00116-3

 
COinS
 

Link to publisher version (DOI)

10.1016/S0893-6080(03)00116-3