Review And Analysis One Of The Approaches To Training The Neural Network Model

dc.contributor.authorBondar, O.
dc.contributor.authorLisovychenko, O.
dc.date.accessioned2022-02-11T09:23:48Z
dc.date.available2022-02-11T09:23:48Z
dc.date.issued2020
dc.description.abstractenThe article provides an overview and analysis one of the approaches to train-ing a neural network model. The list of requirements for training the model was created. As the architecture of a neural network, feedforward networks (perceptron and multilayer percep-tron) were considered and a multilayer perceptron was selected. The input data for training and testing the model were information from the electricity market for one year. Each stage of building and training a model that will serve to predict data was described. The trained model was tested using the mean absolute percentage error (MAPE). Graphs and tables were built that show the dependence of the accuracy of the model on the parameters and input data dur-ing training. The results obtained can be used for practical application, in particular for pre-dicting economic indicators.uk
dc.format.pagerangeС. 66-73uk
dc.identifier.citationBondar, O. Review And Analysis One Of The Approaches To Training The Neural Network Model / O. Bondar, O. Lisovychenko // Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2020. – № 2 (37). – С. 66-73. – Бібліогр.: 9 назв.uk
dc.identifier.doihttps://doi.org/10.20535/1560-8956.37.2020.226815
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/46354
dc.language.isoenuk
dc.publisherКПІ ім. Ігоря Сікорськогоuk
dc.publisher.placeКиївuk
dc.sourceАдаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник, 2020, № 2 (37)uk
dc.subjectartificial neural networkuk
dc.subjectmachine learninguk
dc.subjectdeep learninguk
dc.subjectpredictionuk
dc.subjectfeed-forward networksuk
dc.subjectperceptronuk
dc.subjectmultilayer perceptronuk
dc.subject.udc004.855.5uk
dc.titleReview And Analysis One Of The Approaches To Training The Neural Network Modeluk
dc.typeArticleuk

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