DocumentCode
285247
Title
A guaranteed training of binary pattern mappings using Gaussian perceptron networks
Author
Kwon, Taek M.
Author_Institution
Dept. of Comput. Eng., Minnesota Univ., Duluth, MN, USA
Volume
3
fYear
1992
fDate
7-11 Jun 1992
Firstpage
614
Abstract
Training algorithms are introduced for single- and multiple-layered networks of Gaussian perceptrons. One characteristic of these algorithms is that they can guarantee that a network structure and the corresponding weights will be found for any arbitrarily given mapping relation of binary patterns. A number of computer simulation results are presented to demonstrate the performance of the proposed algorithms
Keywords
feedforward neural nets; learning (artificial intelligence); pattern recognition; Gaussian perceptron networks; binary pattern mappings; computer simulation; guaranteed training; network structure; performance; Backpropagation algorithms; Computer networks; Computer simulation; Feedforward neural networks; Feedforward systems; Neural networks; Neurons; Proposals; Supervised learning; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.227106
Filename
227106
Link To Document