Title :
Expanding the structure of shunting inhibitory artificial neural network classifiers
Author :
Arulampalam, Ganesh ; Bouzerdoum, Abdesselam
Author_Institution :
Edith Cowan Univ., Joondalup, WA, Australia
fDate :
6/24/1905 12:00:00 AM
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
Keywords :
feedforward neural nets; pattern classification; biologically inspired networks; complex nonlinear decision boundaries; feedforward SIANN; shunting inhibitory artificial neural network classifier structure expansion; Adaptive control; Artificial neural networks; Australia; Cellular neural networks; Differential equations; Gain control; Image processing; Multilayer perceptrons; Neurons; Programmable control;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007601