Title

Expanding the Structure Inhibitory Artificial Neural Network Classifiers

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

15

Comments

Originally published as: Arulampalam, G., & Bouzerdoum, A. (2002). Expanding the structure of shunting inhibitory artificial neural network classifiers. Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, Honolulu, USA (pp. 2855-2860). Original paper available here

Abstract

Shunting inhibitory artificial neural networks (SIANNs) are biologically inspired networks in which the neurons interact via a nonlinear mechanism called shunting inhibition. They are capable of producing complex, nonlinear decision boundaries. The structure and operation of feedforward SIANNs and some enhancements are presented. They are applied to several classification problems, and their performance is compared to that of the multilayer perceptron classifier.

DOI

10.1109/IJCNN.2002.1007601

 

Link to publisher version (DOI)

10.1109/IJCNN.2002.1007601