A generalized feedforward neural network architecture for classification and regression
Document Type
Journal Article
Publisher
Elsevier Ltd
Faculty
Faculty of Computing, Health and Science
School
Computing, Health and Science Faculty Office
RAS ID
2330
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
Comments
Arulampalam, G., & Bouzerdoum, A. (2003). A generalized feedforward neural network architecture for classification and regression. Neural networks, 16(5-6), 561-568. Available here