Single-Layer Perceptron Capable Of Classifying 2N+1 Distinct Input Patterns
作者:
AshenayiKaveh,
VoghJames,
MingHeng,
SayehMohammad,
MostafaviMohammad,
期刊:
International Journal of Modelling and Simulation
(Taylor Available online 1990)
卷期:
Volume 10,
issue 4
页码: 124-128
ISSN:0228-6203
年代: 1990
DOI:10.1080/02286203.1990.11760106
出版商: Taylor&Francis
关键词: Neural Networks;Perceptron;Back Propagation;Xor Problem;Parity Check Problem.
数据来源: Taylor
摘要:
AbstractA new multi-threshold Perceptron capable of handling both binary and analog input is presented and discussed. The modified Perceptron replaces the sigmoid function with sum of Gaussian functions with different mean values. A computer program was developed to simulate behavior of a network utilizing the modified Perceptron. Both XOR and parity check problems were solved using a single-layer network utilizing this modified Perceptron. Based on the results obtained from our simulation, the modified Perceptron is capable of solving problems (such as. XOR) that cannot be solved using the classical Perceptrotl. Also, a network utilizing this modified Perceptron reqUIres fewer iterations to converge to a solution than that of a multi-layer network using back propagation.
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