DocumentCode
2595838
Title
Gaussian perceptron: experimental results
Author
Kwon, Taek Mu
Author_Institution
Dept. of Comput. Eng., Minnesota Univ., Duluth, MN, USA
fYear
1991
fDate
13-16 Oct 1991
Firstpage
1593
Abstract
A new neural model which has a Gaussian activation function is presented. This model is referred to as the Gaussian perceptron. For the training of single-layered Gaussian perceptrons, the Gaussian perceptron learning algorithm, which is a variant of the conventional perceptron learning algorithm, is presented. The winner-take-all algorithm is proposed as a multilayer training algorithm. A number of examples are presented along with the comparison with backpropagation networks, which demonstrate the performance of Gaussian perceptron networks
Keywords
learning systems; neural nets; parallel algorithms; Gaussian activation function; Gaussian perceptron learning algorithm; Gaussian perceptron networks; learning systems; multilayer training algorithm; neural nets; winner-take-all algorithm; Associative memory; Backpropagation algorithms; Computer networks; Convergence; Employment; Multi-layer neural network; Nearest neighbor searches; Neural networks; Neurons; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on
Conference_Location
Charlottesville, VA
Print_ISBN
0-7803-0233-8
Type
conf
DOI
10.1109/ICSMC.1991.169917
Filename
169917
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