Setting the hidden layer neuron number in feedforward neural network for an image recognition problem under Gaussian noise of distortion

dc.contributor.author	Romanuke, V.V.
dc.date.accessioned	2014-11-06T18:39:07Z
dc.date.available	2014-11-06T18:39:07Z
dc.date.issued	2013
dc.description.abstract	There is considered an image recognition problem, defined for the single hidden layer perceptron, fed with 5-by-7 monochrome images on its input under Gaussian noise of their distortion. In this neural network the hidden layer neuron number should be set optimally to maximize its productivity. For minimizing traintime duration and recognition error rate both simultaneously there are suggested two ways of solving the corresponding two-objective minimization problem. One of them deals with equilibrium conception, and the other takes Bernoulli criterion for getting the single minimization problem.	uk_UA
dc.identifier.citation	Romanuke V. Setting the Hidden Layer Neuron Number in Feedforward Neural Network for an Image Recognition Problem under Gaussian Noise of Distortion / V. Romanuke // Computer and Information Science. – 2013. – Vol. 6, No. 2. – P. 38-54.	uk_UA
dc.identifier.issn	1913-8989
dc.identifier.uri	https://elar.khmnu.edu.ua/handle/123456789/1839
dc.language.iso	en	uk_UA
dc.subject	feedforward neural network	uk_UA
dc.subject	image recognition	uk_UA
dc.subject	hidden layer neurons	uk_UA
dc.subject	traintime	uk_UA
dc.subject	neural network performance	uk_UA
dc.title	Setting the hidden layer neuron number in feedforward neural network for an image recognition problem under Gaussian noise of distortion	uk_UA
dc.type	Стаття	uk_UA

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