A generalized feedforward neural network architecture for classification and regression

Authors:
Ganesh Arulampalam;Abdesselam Bouzerdoum
Affiliations:
Edith Cowan University, 100 Joondalup Drive, Joondalup, WA 6027, Australia;Edith Cowan University, 100 Joondalup Drive, Joondalup, WA 6027, Australia
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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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.