Title

A generalized feedforward neural network classifier

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

2634

Comments

Originally published as: Arulampalam, G., & Bouzerdoum, A. (2003, July). A generalized feedforward neural network classifier. In Neural Networks, 2003. Proceedings of the International Joint Conference on (Vol. 2, pp. 1429-1434). IEEE. Original article available here

Abstract

In this article a new generalized feedforward neural network (GFNN) architecture for pattern classification is proposed. The GFNNs are an expansion of shunting inhibitory artificial neural networks (SIANNs), proposed previously for classification and function approximations. The GFNN architecture uses as its basic computing unit the generalized shunting neuron (GSN), which includes as special cases the perceptron and the shunting inhibitory neuron. Generalized shunting neurons are capable of forming complex, nonlinear decision boundaries. This allows the GFNN architecture to learn complex pattern classification problems using few neurons. In this article, GFNNs are applied to several benchmark classification problems, and their performance compared to the performance of SIANNs and multilayer perceptrons.

DOI

10.1109/IJCNN.2003.1223906

 
COinS
 

Link to publisher version (DOI)

10.1109/IJCNN.2003.1223906