A generalized feedforward neural network classifier
Faculty of Computing, Health and Science
School of Engineering and Mathematics
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.