Отрывок: Fig.1. Structure of the neuro-fuzzy classifier. First-layer-elements implement the fuzzification operation, in other words, they form the degree of the membership of input data for the defined fuzzy sets ijA :                   2 ' 2 1 exp)'( ij ijj jA сx x ji   , where ijijс , – the parameters of the membership bell-shaped type function. The initial values of these parameters are set so as membership function satisfied the completenes...
Полная запись метаданных
Поле DC Значение Язык
dc.contributor.authorDanilenko, A.N.-
dc.date.accessioned2017-05-25 13:28:15-
dc.date.available2017-05-25 13:28:15-
dc.date.issued2017-
dc.identifierDspace\SGAU\20170522\64062ru
dc.identifier.citationDanilenko A.N. Neural network prediction model of the pilots’ errors // Сборник трудов III международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2017) - Самара: Новая техника, 2017. - С. 1519-1522.ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Neural-network-prediction-model-of-the-pilots’-errors-64062-
dc.description.abstractThis paper introduces a hybrid model of the neuro-fuzzy classifier with integrated prediction of pilots’ mistakes. Experiments and studies of the network were conducted on real and test samples.The upgraded hybrid neuro-fuzzy classifier structure and the learning algorithm can solve the problem of the need for multiple individual performance measurements, the dynamics of which would make it possible to build a trend and solve the problem on small samples. Used in organizational and management activities, this principle can help in predicting the danger caused by the human factor.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.subjectforecastru
dc.subjectwrong actions of the pilotru
dc.subjectintellectual supportru
dc.subjecthybrid neuro-fuzzy classifierru
dc.subjecttwo-layer perceptronru
dc.subjectsmall samplesru
dc.titleNeural network prediction model of the pilots’ errorsru
dc.typeArticleru
dc.textpartFig.1. Structure of the neuro-fuzzy classifier. First-layer-elements implement the fuzzification operation, in other words, they form the degree of the membership of input data for the defined fuzzy sets ijA :                   2 ' 2 1 exp)'( ij ijj jA сx x ji   , where ijijс , – the parameters of the membership bell-shaped type function. The initial values of these parameters are set so as membership function satisfied the completenes...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

Файлы этого ресурса:
Файл Описание Размер Формат  
paper 273_1519-1522.pdfОсновная статья679.04 kBAdobe PDFПросмотреть/Открыть
Показать базовое описание ресурса Просмотр статистики
Поделиться:



Все ресурсы в архиве электронных ресурсов защищены авторским правом, все права сохранены.