Expanding the structure of Shunting Inhibitory Artificial Neural Network classifiers

被引：4

作者：

Arulampalam, G ^{[1
]}

Bouzerdoum, A ^{[1
]}

机构：

[1] Edith Cowan Univ, Joondalup, WA 6027, Australia

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

D O I：

10.1109/IJCNN.2002.1007601

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2855 / 2860

页数：4

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